Modelowanie i korekcja wybranych systemów nieliniowych z zastosowaniem metod analitycznych i aproksymatorów neuronowych

• Autorzy: Jackowska-Strumiłło L.
• Języki publikacji: PL

Abstrakty

PL

Zagadnienia referowane w rozprawie dotyczą modelowania i korekcji nieliniowych własności wybranych obiektów i procesów przemysłowych, z zastosowaniem modeli analitycznych, sztucznych sieci neuronowych (SSN) i modeli hybrydowych. Wykazano, iż zastosowanie SSN do modelowania i korekcji nieliniowych własności rozważanych w pracy obiektów i procesów umożliwia uzyskanie lepszej dokładności niż metody klasyczne, natomiast wykorzystanie wiedzy a priori o obiekcie lub procesie w hybrydowym modelu analityczno-neuronowym może dodatkowo znacznie zmniejszyć złożoność obliczeniową algorytmów: identyfikacji, modelowania i korekcji, w porównaniu z rozwiązaniami stosującymi wyłącznie modelowanie neuronowe. Opracowano algorytmy linearyzacji charakterystyk statycznych termistorów z wykorzystaniem SSN typu perceptron wielowarstwowy (MLP) i modeli hybrydowych, które w szerokim zakresie temperatur są dokładniejsze od klasycznych metod regresji. Omówiono metodę wymuszenia wewnętrznego za pomocą wieloczęstotliwościowych sygnałów binarnych (MBS) i metodę diagramów czasowych do identyfikacji, monitorowania i diagnozowania on-line i in situ własności dynamicznych czujników temperatury. Opisano wybrane zagadnienia związane z projektowaniem sygnałów testowych binarnych i ternarnych. Zasadniczą część rozprawy poświęcono opracowanym metodom modelowania nieliniowych własności dynamicznych czujników temperatury oraz korekcji błędów dynamicznych czujników z zastosowaniem SSN typu MLP, sieci rekurencyjnych i modeli hybrydowych. Wykazano, iż dla czujników o nieliniowych właściwościach dynamicznych modele te zapewniają znacznie lepsząjakość modelowania i korekcji niż klasyczne modele liniowe. Zbudowano cząstkowe neuronowe i hybrydowe modele procesu przędzenia do prognozowania własności przędz na podstawie charakterystyk strumieni zasilających i wybranych parametrów procesu. Osiągnięto znacznie lepszą dokładność modelowania niż przy zastosowaniu klasycznych metod regresji nieliniowej i wielorakiej. SSN zastosowano także do rozwiązania zagadnienia odwrotnego polegającego na identyfikacji inkluzji w obiektach płaskich. Uzyskano krótszy czas obliczeń niż w klasycznych numerycznych metodach iteracyjnych. W ostatniej części rozprawy opisano zintegrowany system komputerowy zaprojektowany dla potrzeb identyfikacji, modelowania i korekcji rozważanych w pracy obiektów i procesów.

EN

The dissertation deals with modelling and correction of nonlinear properties of the selected objects and processes by the use of analytical models, Artificial Neural Networks (ANN) and hybrid models. It was shown that application of ANN for modelling and correction of nonlinear properties of the objects and processes, considered in the thesis, allows for obtaining better accuracy than by means of classical methods. The use of a priori knowledge about an object or a process in a hybrid analytical-neural model can also decrease numerical complexity of identification, modelling and correction algorithms, in comparison to the ANN- based modelling. The proposed algorithms implementing ANN and hybrid models yield in a wide temperature range better linearization results of the thermistors characteristics than the classical regression methods. Self-heating method using the Multifrequency Binary Signals (MBS) and eye patterns method for temperature sensors dynamic properties on-line and in situ identification, monitoring and diagnosis were described. Selected problems concerning binary and ternary testing signals design have been discussed. The significant part of the thesis is dedicated to new methods proposed for modelling of temperature sensors nonlinear dynamic properties and correction of dynamic errors, by the use of MLP, recurrent ANN and hybrid models. It was proved that the proposed models for sensors nonlinear dynamic properties ensure significantly better modelling and correction quality that the classical linear models. Partial ANN and hybrid models of spinning process have been designed, which allow to predict the selected yarn properties on the basis of feeding slivers characteristics and chosen process parameters. Significantly better modelling accuracy has been achieved than by the use of classical nonlinear and multiple regression methods. ANN have been also applied for solving inverse problems, i.e. for identification of inclusion in flat objects. The computing time was shorter than for classical iterative numerical methods. In the last part of the work, an integrated computer system is described. The system was designed for identification, modelling and correction of the objects and processes concerned in the thesis.

Słowa kluczowe

PL

nieliniowe własności; sztuczne sieci neuronowe (SNN); linearyzacja; modelowanie i korekcja nieliniowych własności

EN

nonlinear properties; artificial neural networks; linearization; modelling and correction of nonlinear properties

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Zeszyty Naukowe. Rozprawy Naukowe / Politechnika Łódzka
• Rocznik: 2009
• Tom: Z. 380
• Strony: 3--207
• Opis fizyczny: Bibliog. 341 poz.

Twórcy

autor

Jackowska-Strumiłło L.

• Katedra Informatyki Stosowanej Wydział Elektrotechniki, Elektroniki, Informatyki i Automatyki Politechniki Łódzkiej,

Bibliografia

• Allan G., Fortheringham A., 1996: Optimise your processes and your profits. Technical Textile International. Elsevier Sc. Ltd., Nov., pp. 14-16.
• Alippi, C., Piuri V., 1996: Neural methodology for prediction and identification of nonlinear dynamic systems. Proc. Int. Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, pp. 305-313.
• Andersen K., Cook G. E., Karsai G., Ramaswamy K., 1990: Artificial neural networks applied to arc welding process modeling and control. IEEE Trans. Ind. Applicat., vol. 26, pp. 824-830.
• Ankireddi S., Yang H. T. Y., 1999: Neural networks for sensor fault correction in structural control. Journal of Structural Engineering, Sept. pp. 1056-1064.
• Apolloni B., Piccolboni A., Sozio E., 2001: A hybrid symbolic subsymbolic controller for complex dynamic systems. Neurocomputing, vol. 37, pp. 127-163.
• Arpaia P., Daponte P., Grimaldi D., Michaeli L., 1999: Systematic Error Correction for Experimentally Modeled Sensors by Using ANNs. Proc. of the 16th IEEE Instrumentation and Measurement Technology Conference IMTC'99, Venice, Italy, pp. 1635-1640.
• Arsie I., Pianese C., Sorrentino M., 2006: A procedure to enhance identification of recurrent neural networks for simulating air-fuel ratio dynamics in SI engines. Engineering Applications of Artificial Intelligence, vol. 19, pp. 65-77.
• Aquino W., Brigham J. C., 2006: Self-learning finite elements for inverse estimation of thermal constitutive models. International Journal of Heat and Mass Transfer, vol. 49, pp. 2466-2478.
• Azzouz B., Ben Hassen M., Sakli F., 2007: Quality predictions optimising cotton blend using ANN. The Indian Textile Journal, vol. 117, no. 4, pp. 27-34.
• Babay A., Cheikhrouhou M., Vermeulen B., Rabenasolo B., Castelain M. 2004: Selecting the optimal neural network architecture for predicting cotton yarn hairiness. JOTI, The Textile Institute, vol. 96, no. 3, pp. 185-192.
• Baratti R., Corti S., Servida A., 1997: A feedforward control strategy for distillation columns. Artificial Intelligence in Engineering, vol. 11, pp. 405-412.
• Barker H.A, Godfrey K.R., 1999: System identification with multi-level periodic perturbation signals. Control Engineering Practice, vol. 7, no. 6, pp. 717-726.
• Barlett P.L., 1997: For valid generalization, the size of the weights is more important than the size of the network. Advances in Neural Information Processing Systems, Cambridge MA, MIT Press, vol. 9, pp. 134-140.
• Barron, A.R., 1992: Neural net approximation. Proc. of the Seventh Yale workshop on Adaptive and Learning Systems. New Haven, CT. Yale University, pp. 69-72.
• Barron, A.R., 1993: Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, vol. 39, pp. 930-945.
• Baruch I., Arsenov T., Gortcheva E., Garrido R., 1998: A fuzzy neural model for dynamic system identification and control. Proc. of the 5th Int. Symp. on Methods and Models in Automation and Robotics. MMAR'98. Międzyzdroje, Poland, pp. 661-666.
• Beck J.V., 1985: Inverse Heat Conduction: Ill-Posed Problems, Wiley, New York.
• Bendat J.S., Piersol A.G., 1976: Metody analizy i pomiaru sygnałów losowych. PWN, W-wa.
• Bernieri A., Daponte P., Grimaldi D., 1997: Accurate neural model identification of measurement devices. Measurement, vol. 20, pp. 251-257.
• Beltratti A., Margarita S., Terna P., 1996: Neural Networks for Economic and Financial Modelling. ITCP, London.
• Bhama S., Singh H., 1993: Single Layer Neural Networks for Linear System Identification Using Gradient Descent Technique. IEEE Trans. on Neural Networks, vol. 4, no. 5, pp. 884-888.
• Bhat N. V., Minderman P. A., McAvoy T., Wang N. S., 1990: Modeling Chemical Process Systems via Neural Computation. IEEE Control Systems Magazine, April, pp. 24-30.
• Billings S. A., Wie H.-L., 2005: A New Class of Wavelet Networks for Nonlinear System Identification. IEEE Trans. on Neural Networks, vol. 16, no. 4, July, pp. 862-874.
• Borowik L., 1982: Wyznaczanie temperatury niemierzalnej w sposób ciągły w wybranych obiektach elektrotermicznych. Praca doktorska, Politechnika Łódzka, Łódź. Borowik L., 2003: Pozyskiwanie wiedzy do celów diagnostyki wybranych urządzeń elektrotermicznych w eksperymentach biernych. Rozprawa habilitacyjna, Zeszyty Naukowe, Politechnika Częstochowska, 95, Częstochowa.
• Braun M.W., Ortiz-Mojica R., Rivera D.E., 2002: Application of minimum crest factor multisinusoidal signals for ,,plant-friendly" identification of nonlinear process systems. Control Engineering Practice, vol. 10, no. 3, pp. 301-313.
• Brown M., Harris C., 1994: Neurofuzzy Adaptive Modelling and Control. Prentice Hall, New York, London.
• Brudzewski K., 1998: An attempt to apply Elman's neural network to the recognition of methane pulses. Sensors and Actuators B, 47, pp.231-234.
• van den Bos A., 1967: Construction of multifrequency binary test signals. IF AC Symp. on Identification and Parameter Estimation. Praga, pp. 4-6.
• van den Bos A., 1991: Periodic test signals - Propertie and use. Proc. IEE Int. Conf. CONTROL 91, no. 332, vol. 1, pp. 545-549.
• Bubnicki Z., 1974: Identyfikacja obiektów sterowania. PWN, W-wa.
• Bubnicki Z., 2002: Teoria i algorytmy sterowania. PWN, W-wa.
• Bulsari A. B., 1995: System identification of a linear higher order system. Proc. of First Int. Conf. Engineering Applications of Neural Networks EANN '95, Helsinki, pp. 229-234.
• Burak O., Kandel A., 1996: A hybrid hierarchical neural network-fuzzy expert system approach to chemical process fault diagnosis. Fuzzy Sets and Systems, vol. 83, pp. 11-25.
• Calderon G., Draye J.P., Pavisic D., Teran R., Libert G., 1996: Nonlinear Dynamic System Identification with Dynamic Recurrent Neural Networks. Proc. Int. Workshop on Neural Networks for Ident., Control, Robotics and Signal/Image Proc., Venice, Italy, pp. 49-54.
• Calise A. J., Hovakimyan N., Idan M., 2001: Adaptive output feedback control of nonlinear systems using neural networks. Automatica, vol. 37, pp. 1201-1211.
• Canfield J. C., 2003: Active disturbance cancellation in nonlinear dynamical systems using neural networks. Praca doktorska, University of New Hampshire.
• Chattopadhyay R., 2006: Application of neural network in yarn manufacture. Indian Journal of Fibre & Textile Research, vol. 31, March, pp. 160-169.
• Chen L., Narendra K. S., 2001: Nonlinear adaptive control using neural networks and multiple models. Automatica, vol. 37, pp. 1245-1255.
• Chesnokov V. N., 1996: Fast Training Analog Approximator on the Basis of Legendre Polynomials. Proc. Int. Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, pp. 299-304.
• Choan R. K., Abdullah F., Finkelstein L., 1985: Mathematical modeling of industrial thermometers. Trans. Ins.t MC, vol. 7, no. 3, pp. 151-157.
• Chmielus M., 2000: Moduł programowy sterownika przetwornika analogowo-cyfrowego umożliwiający jego prace. w środowisku Windows. Praca dyplomowa inżynierska, PL, Łódź.
• Chu S. R., Shoureshi R., Tenorio M., 1990: Neural networks for system identification. IEEE Contr. Syst. Mag., vol. 10, Apr., pp. 31-35.
• Ciesielski G., 1994: Modelowanie i korekcja wielowymiarowych stacjonarnych systemów pomiarowych za pomocą operatorów nieliniowych. Rozprawa habilitacyjna, Zeszyty Naukowe, Politechnika Łódzka, 699, Łódź.
• Ciesielski G., Jackowska L., 1989: Microcomputer controlled logarithmic A/D converter for precise thermistor thermometry. IMEKO Symp. on Microprocessors in Temperature and Thermal Measurement. Łódź, pp. 57-63.
• Cimerman F., Blagojevic B., Bajsic L, 2002: Identification of the dynamic properties of temperature sensors in natural and petroleum gas. Sensors and Actuators A, vol. 96, pp. 1-13.
• Cohn D., Tesauro G., 1992: How tight are the Vapnik-Chervonenkis bounds? Neural Computations, vol. 4, pp. 249-269.
• Costa A.C., Alves T.L.M., Henriques A.W.S., Filho R.M., Lima E.L., 1998: An adaptive optimal control scheme based on hybrid neural modelling. Computers chem. Eng. vol. 22, Suppl. pp. S859-S862.
• Cybenko G., 1989: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, vol. 2, pp. 303-314.
• Cyniak D., Czekalski J., Jackowski T., Popin Ł., 2006a: Quality Analysis of Cotton/Polyester Yarn Blends Spun with the Use of a Rotor Spinning Frame. Fibres & Textiles in Eastern Europe, vol.14, no.3,(57), pp 33-37.
• Cyniak D., Czekalski J., Jackowski T., 2006b: Influence of Selected Parameters of the Spinning Process on the State of Mixing of Fibres of a Cotton/Polyester-Fibre Blend Yarn. Fibres & Textiles in Eastern Europe, vol.14, no.4, pp. 36-40.
• Dahlquist G., Bjorck A., 1983: Metody numeryczne, PWN, Warszawa.
• Danisman K., Dalkiran L, Celebi F.V., 2006: Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types. Measurement, vol. 39, pp. 695-700.
• Daponte P., Grimaldi D., 1998: Artificial neural networks in measurements. Measurement, vol. 32, pp. 93-115.
• Das D., Ishiaque S.M., Veil D., Gmes T., 2004: Application of artificial neural network to predict rotor spun yarn properties. Melliand International, vol. 10, no. 3, pp. 183-185.
• Demuth H., Beale M., 1998: Neural Network Toolbox. For Use with MATLAB, User's Guide, Ver. 3, The Math Works Inc.
• Deng H., Li H.-X., Wu Y.H., 2008: Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems. IEEE Trans. Neural Networks, vol.19, no. 9, Sept., pp. 1615-1625.
• Dresler P., 2006: Sterowanie procesem pomiarowym w czasie rzeczywistym na przykładzie systemu komputerowego do pomiaru temperatury. Praca dyplomowa magisterska, PL, Łódź.
• Drożdż A., 2008: Światowy rynek bawełny. Biuletyn Izby Bawełny w Gdyni, nr 4, s. 12-23.
• Dubois O., Nicolas J., Billat A., 1994: Adaptive neural network control of the temperature in an oven. IEE Colloquium on Advances in Neural Networks for Control and Systems, Berlin, 25-27 May 1994, Digest No. 1994/136, pp. 8/1-8/3.
• Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. i inni, 2000: Sieci neuronowe. Tom 6 w: (Red. M. Nałęcz) Biocybernetyka i inżynieria biomedyczna 2000, PAN, Akademicka Oficyna Wydawnicza EXIT, W-wa.
• Duch W., Kacprzyk J., Oja E., Zadrożny S. 2005: Artificial Neural Networks: Formal Models and Their Applications -ICANN2005, 15th Int. Conf. Warsaw, Poland, LNCS 3697, Springer Verlag, Berlin Heidelberg.
• Al-Duwaish, H., Naeem, W., 2001: Nonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms. Proc. of IEEE Int. Conf. on Control Applications, Mexico City, Mexico, pp. 465-469.
• Eckersdorf K., 1980: Optymalizacja układów do korekcji własności dynamicznych elektrycznych czujników termometrycznych. Praca doktorska, Politechnika Łódzka, Łódź.
• Elbuluk M.E., Chan H.W., Husain I., 1998: Neural network controllers for power factor correction of AC/DC switching converters. Proc. IEEE on Industry Application Conference, St. Louis, MO, USA, vol. 3, pp. 1617-1624.
• Elman J.L., 1990: Finding structure in time. Cognitive Science, vol. 14, pp. 179-211.
• Enikov E. T., Lazarov K., 2003: PCB-integrated metallic thermal micro-actuators. Sensors and Actuators A, vol. 105, pp. 76-82.
• Eykhoff P., 1980: Identyfikacja w układach dynamicznych. PWN, W-wa.
• Eykhoff P., 1984: Identification theory: Practical implications and limitations. Measurement, vol. 2, no. 2, pp. 75-84.
• Fernicola, V.C.; Rosso, L., 2000: Time- and frequency-domain analysis of fluorescence lifetime for temperature sensors. Conf. on Precision Electromagnetic Measurements Digest, 14-19 May 2000, pp. 587-588.
• Fujarewicz K., 2000: Identification and suboptimal control of heat exchanger using neural networks. Proc. of the Fifth Conference " Neural Networks and Soft Computing", Polish Neural Network Society & IEEE Neural Networks Council, Zakopane, pp. 469-476.
• Funahashi K., 1989: On the approximate realization of continuous mappings by neural networks. Neural Networks, vol. 2, pp. 183-192.
• Gajda j., Szyper M., 1998: Modelowanie i badania symulacyjne systemów pomiarowych. Wydawnictwa AGH, Kraków.
• Ge S. S., Yang C., Lee T. H., 2008: Adaptive Predictive Control Using Neural Network for a Class of Pure-Feedback Systems in Discrete Time. IEEE Trans. Neural Networks, vol. 19, no. 9, Sept., pp. 1599-1614.
• Gil P., Henriques J., Dourado A., Duarte-Ramos H., 2005: Order Estimation in Affine State-Space Neural Networks. 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications. 28-30 June, Espoo, Finland.
• Godfrey K.R. (Ed), 1993: Perturbations signals for system identification, Cambridge, UK., Prentice-Hall.
• Goldberg D. E., 1995: Algorytmy genetyczne i ich zastosowania. WNT, W-wa.
• Guez A., Eilbert J. L., Kam M., 1988: Neural Network Architecture for Control. IEEE Control Systems Magazine, April, pp. 22-25.
• Guha A., Chattopadhyay R., Jayadeva, 2001: Predicting Yarn Tenacity: A Comparison of Mechanictic, Statistical and Neural Network Models. The Journal of the Textile Institut, vol. 92, no. 2, p. 139-145.
• Hagan, M., Menhaj M., 1994: Training feedforward networks with the Marquardt algorithm. IEEE Trans. on Neural Networks, vol. 5, no. 6, pp. 989-993.
• Hagel R., Zakrzewski J., 1984: Miernictwo dynamiczne. WNT, W-wa.
• Hashemian H.M., Petersen K.M., Mitchell D.W., Hashemian M., Beverly D.D., 1990: In-situ response time testing of thermocouples. ISA Transactions, vol. 29, no. 4, pp. 97-104.
• Hashemian H.M., Petersen K.M., 1992: Loop current step response method for in-place measurement of installed RTDs and Thermocouples. In J.F. Schooley (Ed): Temperature: Its Measurement and Control in Science and Industry. American Institute of Physics, vol. 6, pp. 1151-1156.
• Haykin S., 1999: Neural networks: a comprehensive foundation - 2nd ed. Prentice Hall, USA.
• Hecht-Nielsen R., 1990: Neurocomputing. Addison-Wesley Publishing Company.
• Henderson I.A., McGhee J., 1990: A digital phase-shift technique for narrowband system identification. Trans. Instr MC, vol. 12, no. 3, pp. 147-155.
• Henderson I.A., McGhee J., Smith G., Jackowska-Strumillo L., 1991: Eye patterns and active condition monitoring. Proc. COMADEM '91, Condition Monitoring and Diagnostic Engineering Management, Adam Hilger, pp. 305-309.
• Henderson, I. A., McGhee, J., 1993: Symbolic codes for multifrequency binary testing of control systems, Automatica, vol. 29, no. 6, pp. 1529-1533.
• Henderson, I. A McGhee, J., El-Fandi, M., 1997: Data Measurement. Universities Design and Print, Industrial Control Centre, University of Strathclyde.
• Henderson, I. A., McGhee, J., 1998: Designing multi-frequency ternary test signals using frequency shift keyed modulation. IEEE ConfRec IMTC 98, pp. 1347-1352.
• Henderson I.A., Jackowska-Strummillo L., McGhee J., McGlone P., Sankowski D., 1999: System Identification Using Identification Patterns, in: V. Piuri & M. Savino (ed.), Proc. of the 16th IEEE Instrumentation and Measurement Technology Conference IMTC '99 (Venice, 24-26 May 1999), Italy, pp. 911-916.
• Henderson I.A., Jackowska-Strummillo L., McGhee J., 2000: Digital measurement using shift keyed symbols, in Proc. of XVIIMEKO World Congress, IMEKO 2000, Wiedeń, Editors: M. N. Durakbasa, P.H. Osanna, A. Afjehi-Sadat, Austrian Society for Measurement and Automation, vol. IV, pp. 125-130, (CD-ROM).
• Henriques J., Gil P., Dourado A., 2002 : Neural Output Regulation For a Solar Power Plant. 15th IF AC World Congress, 21-26 July, Barcelona, Spain.
• Hering M., 1980: Termokinetyka dla elektryków. WNT, Warszawa.
• Hering M., 1992: Podstawy elektrotermii. Cz. I. WNT, Warszawa.
• Hering M., 1998: Podstawy elektrotermii. Cz. II. WNT, Warszawa.
• Hertz J., Krogh A., Palmer R., 1995: Wstęp do teorii obliczeń neuronowych. WNT, W-wa.
• Hofmann D., 1976: Dynamische Temperaturmessung. Verlag technik, Berlin.
• Hopfield J. J., 1982: Neural networks and physical systems with emergent collective computational abilities, in Proc. Nat. Acad. Sci., vol. 79, Apr., pp. 2445-2558.
• Hornik K., Stinchcombe M., White H., 1989: Multilayer feedforward networks are universal approximators, Neural Networks, vol. 2, pp. 359-366.
• Hornik K., Stinchcombe M., White H., 1990: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks, vol. 3, pp. 551-560.
• Hush D., Home B., 1993: Progress in supervised neural networks. IEEE Signal Processing Magazine, Jan. pp. 8-38.
• Hussain M. A., 1999: Review of the applications of neural networks in chemical process control -simulation and online implementation. Artificial Intelligence in Eng., vol. 13, pp. 55-68.
• IEEE First International Conference on Neural Networks, 1987, San Diego CA.
• Ikehata M., 1999: How to draw a picture of an unknown inclusion from boundary measurements. Two mathematical inversion algorithms. J. Inv. Ill-Posed Problems, vol. 7, no. 3, pp. 255-271.
• Ilic D., Butorac J., Ferkovic L., 2008: Temperature measurements by means of NTC resistors and a two-parameter approximation curve. Measurement, vol. 41, pp. 294-299.
• Imran M., Bhattacharyya A., 2005: Thermal response of an on-chip assembly of RTD heaters, sputtered sample and microthermocouples. Sensors and Actuators A, vol. 121, pp. 306-320.
• Islam T., Pramanik C., Saha H., 2005: Modeling, simulation and temperature compensation of porous polysilicon capacitive humidity sensor using ANN technique. Microelectronics Reliability, vol. 45, pp. 697-703.
• Jabłoński W., Jackowski T., 1983: Bezwrzecionowe systemy przędzenia. WNT, Warszawa.
• Jabłoński W., Jackowski T., 1986: Technologia przędzalnictwa bawełny. WNT, Warszawa.
• Jabłoński W., Jackowski T., 2001: Nowoczesne systemy przędzenia bazą innowacyjności w procesach wytwarzania przędzy. Beskidzki Instytut Tekstylny, Bielsko-Biała.
• Jackowska-Strumiłło L., 1994: Identyfikacja własności dynamicznych rezystancyjnych czujników temperatury zapomocą Wieloczęstotliwościowych Sygnałów Binarnych. Praca doktorska, Politechnika Łódzka, Łódź.
• Jackowska-Strumiłło L., 1996: Application of artificial neural networks for modelling of RTD dynamics. Third Int. Symp. on Methods and Models in Automation and Robotics. MMAR'96. Międzyzdroje, vol. 3, pp. 1185-1188.
• Jackowska-Strumiłło L., 1997a: Computerised system for RTD dynamics identification. Int. IMEKO Seminar on Low Temperature Thermometry and Dynamic Temperature Measurement. Instytut Niskich Temperatur i Badań Strukturalnych PAN, Wrocław, pp. D-(25-30).
• Jackowska-Strumiłło L., 1997b: Modelowanie i identyfikacja własności dynamicznych rezystancyjnych czujników temperatury metody MBS. III Szkoła - Konferencja Metrologia wspomagana Komputerowo, MWK'97. Zegrze, Tom III, s. 63-68.
• Jackowska-Strumiłło L., 1998a: Identyfikacja ,,in-situ" własności dynamicznych rezystancyjnych czujników temperatury metodą wymuszenia wewnętrznego MBS. PAK, Nr 3, s. 57-60.
• Jackowska-Strumiłło L., 1998b: Modelowanie własności dynamicznych rezystancyjnych czujników temperatury za pomocą perceptronów liniowych. Krajowy Kongres Metrologii, KKM'98, Gdańsk, Tom 2, s. 247-252.
• Jackowska-Strumiłło L., 1998c: Temperature sensors monitoring and diagnosing by the use of MBS data patterns. Proceedings of the 9th International Symposium on System Modelling Control, SMC'98, (CD-ROM), Zakopane, Poland.
• Jackowska-Strumiłło L., 1998d: Komputerowy system pomiarowy do monitorowania własności dynamicznych rezystancyjnych czujników temperatury metodą diagramów czasowych MBS. Sesja Naukowa Wydziału Włókienniczego, Łódź, s. 1-26, 23-24.
• Jackowska-Strumiłło L., 2000: ANN based correction of dynamic errors in temperature measurements. Proc. of the Fifth Conference " Neural Networks and Soft Computing", Polish Neural Network Society & IEEE Neural Networks Council, Zakopane, pp. 681-686.
• Jackowska-Strumiłło L., 2001a: Zastosowanie Sztucznych Sieci Neuronowych do Korekcji Błędów Dynamicznych Wybranych Czujników Pomiarowych. Krajowy Kongres Metrologii, KKM2001- Metrologia u progu trzeciego Millenium, Warszawa, Tom III, s. 767-770.
• Jackowska-Strumiłło L., 2001b: Dynamic temperature error correction by the use of artificial neural networks. The 8' Int. Symp. on Temperature and Thermal Measurements in Industry and Science - TEMPMEKO 2001, 19-21 June 2001, Berlin, Germany, TC-12 IMEKO. Mareriary pokonferencyjne, VDE VERLAG GMBH, Berlin und Offenbach, 2002, vol. 2, pp. 1097-1102.
• Jackowska-Strumiłło L., 2003: Correction of non-linear dynamic properties of temperature sensors by the use of ANN in: Rutkowski L., Kacprzyk J. (Eds.), Neural Networks and Soft Computing Physica-Verlag, Springer Verlag Company, Heidelberg, New York, pp. 837-842.
• Jackowska-Strumilłło L., 2004: ANN based modelling and correction in dynamic temperature measurements in: Rutkowski L., Siekman J., Tadeusiewicz R., Zadeh L. (Eds), Artificial Intelligence and Soft Computing - ICAISC 2004, Springer-Verlag, Berlin Heidelberg, pp. 1124-1129.
• Jackowska-Strumilłło L., 2005: Sztuczne sieci neuronowe w modelowaniu i identyfikacji czujników termometrycznych. Prace naukowe Katedry Informatyki Stosowanej, Zeszyt jubileuszowy 10 lat KIS., Politechnika Łódzka, Łódź, s. 211-219.
• Jackowska-Strumiłło L., McGhee J., Henderson I.A., Muhaisni M., 1992: Instrumentation and experimentation for identification, monitoring and diagnosis of temperature sensors using multifrequency binary sequences. In J.F. Schooley (Ed): Temperature: Its Measurement and Control in Science and Industry. American Institute of Physics, vol. 6, pp. 1043-1048.
• Jackowska-Strumiłło L., Sankowski D., McGhee J., Henderson I.A., 1993: Creation of the dynamic model for RTD in sheath. 5th International Symposium on Temperature and Thermal Measurement in Industry and Science TEMPMEKO'93, Prague, Proc. pp. 279-283.
• Jackowska-Strumiłło L., Sankowski D., McGhee J., Henderson I., 1995a: Simulation of the RTD dynamics using MBS testing. 8th Int. Symp. on System Modelling Control, Polish Society of Medical Informatics, Łódź, vol. 1, pp. 333-338.
• Jackowska-Strumiłło L., Sankowski D., McGhee J., 1995b: Dokładność identyfikacji własności dynamicznych rezystancyjnych czujników temperatury metodą Wieloczęstotliwościowych Sygnałów Binarnych (MBS). Zeszyty Naukowe P.L. Nr 722, Włókiennictwo, z. 52, s. 143-154.
• Jackowska-Strumiłło L., Michalski L., Sankowski D., McGhee J., Henderson I., 1995c: Zastosowanie diagramów czasowych MBS do monitorowania i diagnozowania własności dynamicznych czujników temperatury. VIII Krajowa Konferencja Metrologii, Prace Naukowe Politechniki Warszawskiej, Konferencje, z. 4, t. I, WPW, W-wa, s. 391-396.
• Jackowska-Strumiłło L., Sankowski D., McGhee J., Henderson I.A., 1996a: Simulation of RTD dynamics using MBS testing. System Analysis Modelling and Simulation, OPA (Overseas Publishers Association), Amsterdam, vol. 24, pp. 43-52.
• Jackowska-Strumiłło L., Kucharski J., Sankowski D., 1996b: 'On-line' and 'in-situ' identification of resistance temperature sensors dynamisc by the use of Multifrequency Signals. Third Int. Symp. on Methods and Models in Automation and Robotics. MMAR'96. Międzyzdroje. Wydawnictwo Uczelniane Politechniki Szczecińskiej, Proc., vol. 2, pp. 661-664.
• Jackowska-Strumiłło L., Strumiłło P., 1996c: ANN-based parametric identification of resistance temperature sensors in frequency domain. II Konf. Sieci Neuronowe i ich Zastosowania. Szczyrk, Polskie Towarzystwo Sieci Neuronowych, Częstochowa, Proc., vol. I, pp. 212-217.
• Jackowska-Strumilłło L., Sankowski D., McGhee J., Henderson I.A., 1997: Modelling and MBS experimentation for temperature sensors. Measurement, vol. 20, no. 1, pp. 49-60.
• Jackowska-Strumiłło L., Jackowski T., Chylewska B., Cyniak D., 1998a: Application of a Hybrid Neural Model to Determination of Selected Yarn Parameters. Fibres & Textiles in Eastern Europe, vol. 6, no. 4, pp. 27-32.
• Jackowska-Strumiłło L., Jackowski T., Chylewska B., Cyniak D., 1998b: Hybrid neural modelling approach to determination of selected yarn parameters. Proc. of the Fifth Int. Symp. on Methods and Models in Automation and Robotics, MMAR '98, Międzyzdroje, Poland, pp. 633 - 638.
• Jackowska-Strumiłło L., Henderson I., McGhee J., Sankowski D., 1999a: Condition monitoring of temperature sensors using multifrequency identification patterns. Proc. of The 7th Int. Symp. on Temperature and Thermal Measurements in Industry and Science -TEMPMEKO'99, Delft, The Netherlands, J. Dubbeldam, M. de Groot (Editors), NMi Van Swinden Laboratorium & TC-12 IMEKO, pp. 441-446.
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., 1999b: The Topological Derivative Method and Artificial Neural Networks for Numerical Solution of Shape Inverse Problems. Raport INRIA Lorraine Inst., NrRR-3739, czerwiec 1999, (18 stron), wygłoszony też jako referat na 19th IFIP TC7 Conf. on System Modelling and Optimization, Cambridge, UK, Book of Abstracts, s. 61.
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., 1999c: The Topological Derivative Method in Shape Optimization. Proc. of 38th IEEE Conference on Decision and Control -CDC'99, Phoenix, USA, IEEE Control System Society, CD-ROM, pp. 674-679.
• Jackowska-Strumiłło L., Jackowski T., Kapczynski W., 2000a: Computerised system for spinning process monitoring. Proc. of XVI IMEKO World Congress, IMEKO 2000, Wiedeń, Editors: M. N. Durakbasa, P.H. Osanna, A. Afjehi-Sadat, Austrian Society for Measurement and Automation, vol. VI, pp. 197-202.
• Jackowska-Strumiłło L., Jackowski T., Cyniak D., Chylewska B., 2000b: Hybrid neural models for determination of yarn parameters. Proceedings of the Third Polish Conference on theory and Applications of Artificial Intelligence Colloquia in Artificial Intelligence Theory and Applications - CAI 2000, Lodz, pp. 175-187.
• Jackowska-Strumiłło L., McGlone P., Henderson L, McGhee J., Sankowski D., 2000c: Investigating the Self-Heating Test for a Temperature Sensor Using a Ternary Identification Pattern. Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference IMTC"00, Baltimore, Maryland, USA, vol. 1, pp. 132-136, (CD-ROM).
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., Henrot A., 2000d: On numerical solution of shape inverse problems. Publikacje Instytutu Elie Cartan, Universite Henri Poincare Nancy, no. 24., pp. 1-24.
• Jackowska-Strumiłło L., Urbański M., 2001: Linearisation of thermistor characteristic by the use of artificial neural networks. The 8th Int. Symp. on Temperature and Thermal Measurements in Industry and Science - TEMPMEKO 2001, 19-21 June 2001, Berlin, Germany, TC-12 IMEKO. Mareriary pokonferencyjne, VDE VERLAG GMBH, Berlin und Offenbach, 2002, vol. 2, pp. 1035-1040.
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., 2002: On numerical solutions of shape inverse problems, Computational Optimisation and applications, Kluwer Academic Publishers, vol. 23, no. 2, pp. 231-255.
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., 2003: Topological optimization and inverse problems. Computer Assisted Mechanics and Engineering Sciences, vol. 10, no. 2, pp. 163-176.
• Jackowska-Strumiłło L., Jackowski T., Cyniak D., Czekalski J., 2004: Neural Model of the Spinning Process for Predicting Selected Properties of Flax/Cotton Yarn Blends. Fibres & Textiles in Eastern Europe, ISSN 1230-3666, vol. 12, no. 4, pp. 17-21.
• Jackowska-Strumiłło L., Sokolowski J., Zochowski A., 2005: Topological Derivative and Training Neural Networks for Inverse Problems, in Artificial Neural Networks: Biological Inspirations - ICANN 2005, (Eds: Duch W., Kacprzyk J., Oja E., et al.): Lecture Notes in Computer Science, Volume 3697/2005, Springer-Verlag GmbH, pp. 391-396.
• Jackowska-Strumiłło L., Cyniak D., Czekalski J., Jackowski T., 2007a: Quality of Cotton Yarns Spun Using Ring-, Compact-, and Rotor-Spinning Machines as a Function of Selected Spinning Process Parameters. Fibres & Textiles in Eastern Europe, vol. 15, no. 1, pp. 24-30.
• Jackowska-Strumiłło L., Cyniak D., Czekalski J., Jackowski T., 2007b: Modelling the Dependencies Between the Structure of Feeding Streams and the Parameters of Cotton/Polyester Blended Yarns Manufactured with Use of Ring- and Rotor Spinning Machines. Fibres & Textiles in Eastern Europe, vol. 15, no. 3, pp. 30-35.
• Jackowska-Strumiłło L., Cyniak D., Czekalski J., Jackowski T., 2008: Neural Model of the Spinning Process Dedicated to Predicting Properties of Cotton-Polyester Blended Yarns on the Basis of the Characteristics of Feeding Streams. Fibres & Textiles in Eastern Europe, vol. 16, no. 1, pp. 28-36.
• Jackowski T, Chylewska B., Cyniak D., 1994: The Hairiness of Yarns from Cotton and Cotton Type Fibres. Fibres and Textiles in Eastern Europe, vol 2, nr 2, s. 22-23.
• Jackowski T., Kowalski K., Chylewska B., Cyniak D., Klata Z., 1995a: A Microcomputer System of Measurement and Analysis of the Dynamic Forces in Yarn during the Rotor Spinning Process. Fibres & Textiles in Eastern Europe, vol. 3, no. 1, pp. 36-38.
• Jackowski T., Kowalski K., Chylewska B., Cyniak D., Rowińska Z., 1995b: Assesment of the dynamic tension in open end spinning and parameters of yarns. Fibres & Textiles in Eastern Europe, vol. 3, no. 2, pp. 42-44.
• Jackowski T., Chylewska B., Cyniak D., Rowińska Z., and Perka I., 1996: Tension in Rotor Yarns with Respect to Density Distribution in a Modelled Feed Stream of Fibres. Fibres & Textiles in Eastern Europe, vol. 4, no. 3, pp. 52-55.
• Jackowski T., Jackowska-Strumiłło L., Chylewska B., Cyniak D., 1998a: Using Artificial Neural Networks for Determination of Irregularity of Fibre Mass Distribution in Rotor-Spun Yarns. Proceedings of the Vh International Conference IMTEX'98, Łódź, Plenary Session, pp. 1-7.
• Jackowski T., Jackowska-Strumiłło L., Chylewska B., Cyniak D., 1998b: Wykorzystanie sztucznych sieci neuronowych w badaniach przędz rotorowych. Przegląd Włókienniczy, Nr 9, s. 14-18.
• Jackowski T., Jackowska-Strumiłło L., Chylewska B., Cyniak D., Kapczynski W., 1999: Komputerowy system pomiarowy do rejestracji parametrów procesu przędzenia. VII Konf. Sieci i Systemy Informatyczne SIS'99, Politechnika Lodzka, Lodz, pp. 287-293.
• Jackowski T., Chylewska B., Cyniak D., Jackowska-Strumiłło L., 2002: Investigating and modeling yarn parameters for different feeding slivers. Textiles and Fibres for Eastern Europe, vol. 10, no. 1, pp. 49-51.
• Jackowski T., Chylewska B., Cyniak D., Jackowska-Strumiłło L., 2003: Modelling of the relationship between feeding sliver structures and parameters of cotton/linen blended yarns. Textiles and Fibres for Eastern Europe, ISSN 1230-3666, vol. 11, no. 2 (41), pp. 12-17.
• Jackowski T., Kapczyński W., Jackowska-Strumiłło L., Chylewska B., Cyniak D., 2004: Patent na rzecz Politechniki Łódzkiej, Łódź, Polska, nr 186680, na wynalazek pt. ,,Urządzenie do pomiaru parametrów procesu przędzenia", Warszawa, dn. 17.05.2004.
• Jamro E., Wiatr K., 2005: A Novel Parallel-Serial Architecture for Neural Networks Implemented in FPGAs, Proc. of the 8th IEEE Workshop on Design and Diagnostics of Electronic Circuits Systems, Sopron, pp. 121-128.
• Kabziński J., Marsjan K., 2003: MIMO nonlinear adaptive tracking control with AI reconstruction of input gains. Proc. of the 9th IEEE Int. Conf. on Methods and Models In Automation and Robotics MMAR 2003, Międzyzdroje, Poland, pp. 511-516.
• Kaczorek T., 1981: Teoria sterowania. PWN, W-wa.
• Kamiński W., Strumiłło P., Zbiciński L, 1996: Hybrid approach to network modelling in process engineering, Second Conf ,,Neural Networks and Their Applications ", Szczyrk, pp. 255-266.
• Kamiński W., Strumiłło P., Tomczak E., 2005: Zastosowanie systemów sztucznej inteligencji w rozwiązywaniu wybranych problemów ochrony atmosfery. Polska Akademia Nauk, Oddział w Łodzi, Łódź.
• Karpinski M., Macintyre A., 1997: Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neuronal networks. Journal of Computer and System Science, vol. 54, pp. 169-176.
• Kawato M., Uno Y., Isobe M., Suzuki R., 1988: Hierarchical Neural Network Model for Voluntary Movement with Application to Robotics. IEEE Control Systems Magazine, April, pp. 8-16.
• Kecman V., 1996: System Identification Using Modular Neural Network With Improved Learning. Proc. Int. Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, pp. 40-48.
• Kerlin T.W., Miller L.F., Hashemian H.M., 1978: In-situ response time testing of platinum resistance thermometers. ISA Transactions, vol. 17, no. 4, pp. 71-88.
• Kerlin T.W., Hashemian H.M., Petersen K.M., 1982a: Response characteristics of temperature sensors installed in processes, in G. Striker, K. Havrilla, J. Solt and T. Kemeny (eds.), Technological and Methodological Advances in Measurement, ACTA IMEKO 1982, North-Holland Publ. Comp.,1982, vol. Ill, pp. 95-103.
• Kerlin T.W., Shepard R.L., Hashemian H.M., Petersen K.M., 1982b: Response of installed temperature sensors. Proc. VI Int. Symp.: Temperature, its Measurement and Control in Science and Industry. Washington, American Institute of Physics, pp. 1357-1366.
• Kempczyński P., 2005: Modelowanie rekurencyjnych sztucznych sieci neuronowych typu Hopfielda w języku Java. Praca dyplomowa magisterska, PL, Łódź.
• Keskin A. U., 2005: A simple analog behavioural model for NTC thermistors including selfheating effect. Sensors and Actuators A, vol. 118, pp. 244-247.
• Khadir M.T., Ringwood J.V., 2003: Linear and nonlinear model predictive control design for a milk pasteurization plant. Control and Intelligent Systems, Vol. 31, No. l, pp. 1-8.
• Khalid M., Omatu S., 1992: A Neural Network Controller for Temperature Control System. IEEE Control Systems Mag. vol. 12, June, pp. 58-64.
• Khalid M., Omatu S., 1995: Temperature Regulation with Neural Networks and Alternative Control Schemes. IEEE Tram. Neural Networks, vol. 6, no. 3, pp. 572-582.
• Khan S. A., Shahani D. T., Agarwala A. K., 2003: Sensor calibration and compensation using artificial neural network. ISA Transactions, vol. 42, pp. 337-352.
• Kim Y.I., Moon K.C., Kang B.S., Han C., Chang K.S., 1998: Application of neural network to the supervisory control of a reheating furnace in the steel industry. Control Engineering Practice, vol. 6, pp. 1009-1014.
• Kleiber M., Mang H.A (Eds.), Garstecki A., Łodygowski T., Sokołowski J. (Guest Eds.), 2003: Optimal design of materials and structures. Special Issue. Computer Assisted Mechanics and Engineering Sciences, vol. 10, no. 2.
• Klimasauskas C. C., 1998: Hybrid modeling for robust nonlinear multivariable control. ISA Transactions., vol. 37, pp. 291-297.
• Knudsen N.O., 1999: A Method for Linearization of a Multibit Delta-Sigma (??) A/D Converter. Proc. of the 16th IEEE Instrum. & Meas. Tech. Conf. IMTC'99, Venice, Italy, pp. 1625-1628.
• Kohonen T., 1984: Self-organization and associative memory. Springer Verlag, Berlin.
• Kong J.S., Maute K., Frangopol D.M., Liew L.-A., Saravanan R.A., Raj R., 2003: A real time human-machine interface for an ultrahigh temperature MEMS sensor-igniter. Sensors and Actuators A, vol. 105, pp. 23-30.
• Korbicz J., Obuchowicz A., Uciński D., 1994: Sztuczne sieci neuronowe, podstawy i zastosowania. Akademicka Oficyna Wydawnicza PLJ, Warszawa.
• Kowalski K., 1991: Identyfikacja dynamicznych sił w nitkach na szydełkarkach na podstawie symulacji komputerowej i cyfrowej techniki pomiarowej, Rozprawa habilitacyjna, Zeszyty Naukowe PL, 147, Łódź.
• Kraszewski M., 2004: Modelowanie sztucznych sieci neuronowych typu perceptron wielowarstwowy w języku Java. Praca dyplomowa magisterska, PL, Łódź.
• Kucharski J., 1993: Adaptacyjna regulacja temperatury wybranych urzqdzeń elektrotermicznych. Praca doktorska, Politechnika Łódzka, Łódź.
• Kucharski J., 2003: Wykorzystanie logiki rozmytej i informacji niedoskonałych w pomiarach i regulacji temperatury rezystancyjnych urządzeń elektrotermicznych. Rozprawa habilitacyjna, Zeszyty Naukowe, Politechnika Łódzka, 923, Łódź.
• Kulesza W., Bergander T., McGhee J., Hultgren A., Ingelbrant P., Knells M., Wirandi J., 2000: An In-situ Real Time Impulse Response Auto-Test of Resistance Temperature Sensors. Proc. of the 17th IEEE IMTC2000, vol. I, pp. 143-147.
• Kuo C.F.J., Wang C.C., Hsieh C.T., 1999: Theoretical Control and Experimental Verification of Carded Web Density. Part III: Neural Network Controller Design. . Textile Res. J. vol. 69, no. 6, pp. 401-406.
• Kuźma E., 1974: Termometria termistorowa. PWN, W-wa.
• Kwan C., Lewis F. L., Dawson D. M., 1998: Robust neural-network control of rigid-link electrically driven robots. IEEE Trans. Neural Networks, vol. 9, July, pp. 581-588.
• Kwaśniewski J., 1993: Wprowadzenie do inteligentnych przetworników pomiarowych. WNT, W-wa.
• Larminat P., Thomas Y., 1993: Automatyka - układy liniowe. Tom II Identyflkacja. WNT, W-wa.
• Lee Y. W., 2007: Neural solution to the target intercept problems in a gun fire control system. Neurocomputing, vol. 70, pp. 689-696.
• Levin A. U., Narendra K. S., 1993: Control of nonlinear dynamical systems using neural networks: Part II - Identification and Part III - Control. IEEE Trans. Neural Networks, vol. 4, Mar., also IEEE Trans. Neural Networks, vol. 7, Jan. 1996, pp. 30-42;
• Lewandowski S., Stańczyk T., 2005: Identification and Classification of Spliced Wool Combed Yarn Joints by Artificial Neural Network. Fibres & Textiles in Eastern Europe, part. I: vol.13, no. 1, pp. 39-43, part II: vol. 13, no. 2, pp. 16-19.
• Lecoeuche S., Tsaptsinos D. (Eds.), 2006: Engineering Applications of Neural Networks - Novel Applications of Neural Networks in Engineering. Special issue, Engineering Applications of Artificial Intelligence, vol. 19, no. 7.
• Liang C.-K., Tsai C.-C., 2005: Evaluation of a novel PTC thermistor for telecom overcurrent protection. Sensors and Actuators A, vol. 121, pp. 443-449.
• Liew L.-A., Bright V. M., Raj R., 2003: A novel micro glow plug fabricated from polymer-derived ceramics: in situ measurement of high-temperature properties and application to ultrahigh-temperature ignition. Sensors and Actuators A, vol. 104, pp. 246-262.
• Lightbody G., Irwin G.W., 1996: Multi-layer perceptron based modeling of nonlinear systems. Fuzzy Sets and Systems, vol. 79, pp. 93-112.
• Lindskog P. and Sjoberg J., 1995: A comparison between semi-physical and black-box neural net modeling: a case study. Proc. of First Int. Conf. Engineering Applications of Neural Networks EANN '95, Helsinki, pp. 235-237.
• Lippmann R., 1987: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine, April, pp. 4-22.
• Majumdar P. K., Majumdar A., 2004: Predicting the Breaking Elongation of Ring Spun Cotton Yams Using Mathematical, Statistical and Artificial neural Network Models. Textile Research Journal, vol. 74, no. 7; pp. 652-655.
• Majumdar M., Majumdar P.K., Sarker B., 2006: An investigation on yarn engineering using artifical neural networks. Journal of the Textile International, vol. 97, no. 5, p. 429.
• Maląg; M., 2000: Komputerowy układ do pomiaru temperatury z linearyzacjq charakterystyk statycznych wybranych czujników termometrycznych. Praca magisterska, PL, Łódź.
• Mańczak K., Nahorski Z., 1983: Komputerowa identyfikacja obiektów dynamicznych. PWN, W-wa.
• Martinez-Cisneros C. S., Ibanez-Garcia N., Valdes F., Alonso J., 2007: LTCC microflow analyzers with monolithic integration of thermal control. Sensors and Actuators A, vol. 138, pp. 63-70.
• Massicotte D., Megner B., 1999: Neural network based method of correction in a nonlinear dynamic measuring system, in V. Piuri and M. Savino (eds.), Proc. 16th IEEE Instrumentation and Measurement Technology Conf. IMTC'99, Venice, Italy, pp. 1641-1646.
• Materka A., 1995a: On noise induced error of system parameter estimation using Artificial Neural networks. Proc. 8th Int. Symp. on System Modelling Control (SMC'95), Polish Society of Medical Informatics, Łódź, vol. 3, pp. 85-90.
• Materka A., 1995b: Time-delay estimation: Least-Squared-Error versus Neural-Network-Based techniques. 8th Int. Symp. on System Modelling Control, Polish Society of Medical Informatics, Łódź, vol. 3, pp. 79-84.
• Mauris G., Foulloy L., 2001: A Fuzzy Symbolic Approach to Formalize Sensory Measurements An Application to a Comfort Sensor. Proc. of the 18th IEEE IMTC 2001, Budapest, s. 1413-1417.
• McCulloch W. S., Pitts W., 1943: A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., vol. 5, pp. 115-133.
• McGhee J., Fisher G., Henderson I.A., 1987: A fast DFT algorithm for on-line MBS process identification. Proc. CSCS 8, 7th Int. Conf. Cont Sys. Comp. Sc., Politechnical Institute of Bucharest, pp. 111-129.
• McGhee J., Sheppard D.A., Henderson I.A., 1989: Immersion testing of temperature sensors using a microprocessors based MBS generator/analyser. ACTA IMECO, Lodz, pp. 65-72.
• McGhee J., Jackowska-Strumiłło L., Henderson I., McGowan M., 1991a: Using Strathclyde Multi-frequency Binary Sequences for Temperature Sensor Diagnosis. COMADEM'91: Condition Monitoring and Diagnostic Engineering Management, Adam Hilger, pp. 300-304.
• McGhee J., Henderson I.A., Jackowska-Strumiłło L., 1991b: Identifying temperature sensors using Strathclyde Compact Multifrequency Binary Sequences. Proc. CSCS 8, 8th Int. Conf. Cont Sys. Comp. Sc., Politechnical Institute of Bucharest, pp. 144-152.
• McGhee J., Michalski L., Henderson I.A., Eckersdorf K., Sankowski D., 1992: Dynamic properties of control temperature sensors (part I & II). In J.F. Schooley (Ed): Temperature: Its Measurement and Control in Science and Industry. American Inst, of Physics, vol. 6, pp. 1157-1168.
• McGhee J., Henderson I.A., Jackowska-Strumiłło L., 1993: Temperature sensor identification with multifrequency binary sequences. Rozdział 9 w: Godfrey K.: Perturbations signals for system identification. Prentice-Hall, University Press, Cambridge, UK, pp. 277-297.
• McGhee J., Korczyński J., Henderson I.A., Kulesza W., 1996: Scientific Metrology. Technical University of Lodz.
• McGhee J., Henderson I.A., Baird A., 1997: Neural networks applied for the identification and fault diagnosis of process valves and actuators. Measurement, vol. 20, pp. 267-275.
• McGhee J., Henderson LA., Sydenham P.H., 1999: Sensor science - essential for instrumentation and measurement. Measurement, vol. 25, pp. 89-113.
• McGhee J., Kulesza W., Korczyński J., Henderson LA., 2001: Measurement data handling, Theoretical technique. Technical University of Lodz.
• Meireles M. R. G., Almeida P. E. M., Simoes M. G., 2003: A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. on Industrial Electronics, vol. 50, no. 3, pp. 585-601.
• Merlone A., Iacomini L., Tiziani A., Marcarino P., 2007: A liquid bath for accurate temperature measurements. Measurement, vol. 40, pp. 422-427.
• Michalski L., Eckersdorf K., 1990: In-situ determination of dynamics of temperature sensors. Proc. TEMPMEKO'90, Helsinki, pp. 193-200.
• Michalski L., Kuźmiński K., Sadowski J., 1981: Regulacja temperatury urządzeń elektrotermicznych. PWN, W-wa.
• Michalski L., Eckersdorf K., McGhee J., 1991: Temperature Measurement. John Wiley and Sons Ltd., Chichester.
• Michalski L., Eckersdorf K., Kucharski J., Sankowski D., Urbanek P., 1993: Experimental computerized in-situ identification of thermocouple sensors. 5th Int. Symp. on Temperature and Thermal Measurement in Industry and Science TEMPMEKO'93, Prague, pp. 107-111.
• Michalski L., Eckersdorf K., Kucharski J., 1998: Termometria Przyrządy i Metody. Politechnika Łódzka, Łódź.
• Minkina W., 1999: Theoretical and experimental identification of the temperature sensor unit step response non-linearity during air measurement. Sensors and Actuators A, vol. 78, pp. 81-87.
• Minkina W. I Gryś S., 1999: Cyfrowa korekcja ,,sztywna" dynamicznego przetwarzania termometru. PAK, nr 6, s. 21-24.
• Morcego, B.; Fuertes, J.M.; Cembrano, G., 1996: Neural modules: networks with constrained architectures for nonlinear function identification. Proc. Int. Workshop on Neural Networks for Identification Control Robotics and Signal/Image Processing, Venice, Italy, pp. 290-298.
• Moghavvemi M., Ng K.E., Soo C.Y., Tan S.Y., 2005: A reliable and economically feasible remote sensing system for temperature and relative humidity measurement. Sensors and Actuators A, vol. 117, pp. 181-185.
• Mroczka J. (Ed.), 2008: Problemy metrologii elektronicznej i fotonicznej. Oficyna wydawnicza Politechniki Wrocławskiej, Wrocław.
• Narendra K.S., Lewis F.L. (Eds.), 2001: Neural Networks Feedback Control. Special issue, Automatica, vol. 37.
• Narendra K.S., Mukhopadhyay S., 1997: Adaptive control using neural networks and aproximate models, IEEE Trans. Neural Networks, vol. 8, no. 3, pp. 475-485.
• Narendra K.S., Parthasarathy K., 1990: Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4-27.
• Nauck D., Klawonn F., Kruse R., 1997: Neuro-Fuzzy Systems. John Willey & Sons, Chichester.
• Nerrand O., Roussel-Ragot P., Urbani D., Personnaz L., Dreyfus G., 1994: Training Recurrent Neural Networks: Why and How? An Illustration in Dynamical Process Modelling. IEEE Trans. on Neural Networks, vol. 5, no. 2, pp. 178-184.
• Niederliński A., 1977: Systemy cyfrowe automatyki przemyslowej. WNT, W-wa.
• Noyan Ogulata S., Cenk Sahin, Tugrul Ogulata R., Onur Balci, 2006: The Prediction of Elongation and Recovery of Woven Bi-Stretch Fabric Using Artifical Neural Network and Linear Regression Models. Fibres & Textiles in Eastern Europe, no. 2, vol. 14, pp. 46-49.
• Ogiela M. R., Tadeusiewicz R., 2008: Modern Computational Intelligence Methods for the Interpretation of Medical Image, Studies in Computational Intelligence, vol. 84, SpringerVerlag, Berlin-Heidelberg-New York.
• Oppenheim A.V., Schafer R.W., 1979: Cyfrowe przetwarzanie sygnałów. WKiŁ, W-wa.
• Orłowski K., 2007: Oprogramowanie do symulacji własności dynamicznych obiektów za pomocą sztucznych sieci neuronowych. Praca dyplomowa magisterska, PŁ, Łódź.
• Osowski St., 1996: Sieci neuronowe w ujęciu algorytmicznym. WNT, W-wa.
• Osowski St., 2000: Sieci neuronowe do przetwarzania informacji. Oficyna Wydawnicza Politechniki Warszawskiej, W-wa.
• Parker D. B., 1986: A comparison of algorithms for neuron-like cells, in: Neural Networks for Computing, Denker J. S. Ed. American Institute of Physics, New York, pp. 327-332.
• Patora T., 2002: Komputerowy wielokanałowy układ do pomiaru temperatury z wykorzystaniem układów AD I860. Praca dyplomowa magisterska, PŁ, Łódź.
• Patra J. C., 1997: An artificial neural network-based smart capacitive pressure sensor. Measurement, vol. 22, pp. 113-121.
• Patra J. C., van den Bos A., 1999: Modeling and development of an ANN-based smart pressure sensor in a dynamic environment. Measurement, vol. 26, pp. 249-262.
• Patra J. C., van den Bos A., 2000a: Modeling of an intelligent pressure sensor using functional link artificial neural networks. ISA Transactions, vol. 39, pp. 15-27.
• Patra J. C., van den Bos A., 2000b: Auto-calibration and -compensation of a capacitive pressure sensor using multilayer perceptrons. ISA Transactions, vol. 39, pp. 175-190.
• Patra J. C., van den Bos A., Kot A. C., 2000: An ANN-based smart capacitive pressure sensor in dynamic environment. Sensors and Actuators, vol. 86, pp. 26-38.
• Pełczewski W., 1980: Teoria sterowania. WNT, W-wa.
• Poggio T., Girosi F., 1990: Networks for Approximation and Learning. Proceedings of the IEEE 78, pp. 1481-1497.
• Postoache, O.; Girao, P.S.; Ramos, H.G.; Pereira, J.M.D., 1998: A temperature sensor fault detector as an artificial neural network application. Proc. of 9th Mediterranean Electrotechnical Conference, MELECON98, 18-20 May 1998, vol. l, pp. 678-682.
• Potentas W., 2009: Implementacja wybranych algorytmów przetwarzania sygnałów w środowisku Photon/QNX. Praca dyplomowa magisterska, PŁ, Łódź.
• Poznyak A. S., Ljung L., 2001: On-line identification and adaptive trajectory tracking for nonlinear stochastic continuous time systems using differential neural networks. Automatica, vol. 37, pp. 1257-1268.
• Psaltis D., Sideris A., Yamamura A. A., 1988: A Multilayered Neural Network Controller. IEEE Control Systems Magazine, April, pp. 17-21.
• Pynckels F., Kiekens P., Sette S., Van Langenhove L., Impe K., 1995: Use of Neural Nets for Determining the Spinnability of Fibres. Journal of the Textile Inst., vol. 86, no. 3, pp. 425-437.
• Pynckels F., Kiekens P., Sette S., Van Langenhove L., Impe K., 1997: The Use of Neural Nets for Simulate the Spinning Process. Journal of the Textile Institute, vol. 88, no. 4, pp. 440-447.
• Qin S.-Z. Su H.T., McAvoy T.J., 1992: Comparison of Four Neural Net Learning Methods of Dynamic System Identification. IEEE Transactions on Neural Networks, vol. 3, no. 1, pp. 122-130.
• Ramesh M.C., Rajamanickam R., Jayaraman S., 1995: The Prediction of Yarn Tensile Properties by Using Artificial Neural Networks. Journal of the Textile Institute, vol. 86, no. 3, pp. 459-469.
• Roche J.R., Sokołowski J., 1996: Numerical methods for shape identification problems. Special issue of Control and Cybernetics: Shape Optimisation and Scientific Computations, vol. 5, pp. 867-894.
• Rumelhart D. E., Hinton G. E., Williams R. J., 1986: Learning representations by back-propagating errors. Nature, vol. 323, pp. 533-536.
• Rutkowska D., 1997: Inteligentne systemy obliczeniowe. Algorytmy genetyczne i sztuczne sieci neuronowe w systemach rozmytych. Akademicka Oficyna Wydawnicza PLJ, W-wa.
• Rutkowska D., 2002: Neuro-Fuzzy Architectures and Hybrid Learning. Physica-Verlag, Springer Verlag Company, Heidelberg, New York.
• Rutkowska D., Piliński M., Rutkowski L., 1999: Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. PWN, W-wa.
• Rutkowski L., 1994: Filtry adaptacyjne i adaptacyjne przetwarzanie sygnałów. WNT, W-wa.
• Rutkowski L., 2004a: New Soft Computing techniques for system modelling. In: Pattern Classification and Image Processing. Springer Verlag, Heidelberg, New York.
• Rutkowski L., 2004b: Flexible neuro-fuzzy systems. Structures, learning and performance evaluation. Kluwer Academic Publishers, Boston/Dordrecht/London.
• Rutkowski L., 2005: Metody i techniki sztucznej inteligencji. PWN, W-wa.
• Rutkowski L., Kacprzyk J. (Eds.) 2003: Neural Networks and Soft Computing. Physica-Verlag, Springer Verlag Company, Heidelberg, New York.
• Rutkowski L., Siekman J., Tadeusiewicz R., Zadeh L. (Eds) 2004: Proc. of the Seventh Int. Conf. Artificial Intelligence and Soft Computing, ICAISC 2004, Springer-Verlag, Berlin Heidelberg, New York.
• Rutkowski L., Tadeusiewicz R., Zadeh L., Zurada J. (eds.), 2008a: Artificial Intelligence and Soft Computing - ICAISC 2008, Lecture Notes in Artificial Intelligence, vol. 5097, Springer-Verlag, Berlin-Heidelberg-New York.
• Rutkowski L., Tadeusiewicz R., Zadeh L., Zurada J. (eds.), 2008b: Computational Intelligence: Methods and Applications, EXIT, W-wa.
• Ryoo Y.J., Lim Y.C., Kim K.H., 2001: Classification of materials using temperature response curve fitting and fuzzy neural network. Sensors and Actuators A, vol. 94, pp. 11-18.
• Al-Salaymeh A., Ashhab M.S., 2006: Modelling of a novel hot-wire thermal flow sensor with neural nets under different operating conditions. Sensors and Actuators A, vol. 126, pp. 7-14.
• Sankowski D., 1989: Wykorzystanie Wieloczęstotliwościowych Sygnałów Binarnych (MBS) do identyfikacji 'on-line' rezystancyjnych urzadzeń grzejnych. Rozprawa habilitacyjna, Zeszyty Naukowe, Politechnika Łódzka, 569, Łódź.
• Sankowski D., McGhee J., Henderson L, Kucharski J., Urbanek P., 1993: Application of Multi-frequency Binary Signals for identification of electric resistance furnaces., Rozdzial 8 w: Godfrey K.: Perturbations signals for system identification. Prentice-Hall, University Press, Cambridge, UK, pp. 255-276.
• Sankowski D., Michalski L., Eckersdorf K., Kucharski J., 1995a: The use of MBS test signals for determination of thermocouple sensor dynamics. Proc. IEEE Instrumentation and Measurement Technology Conference IMTC"95 Boston, USA, pp. 619-623.
• Sankowski D., Kucharski J., Jackowska-Strumiłło L., Szaruga P., 1995b: Przenośny układ pomiarowo-badawczy dla potrzeb diagnozowania urządzeń elektrotermicznych. VIII Krajowa Konf. Metrologii. Prace Naukowe PW, z.4, t. II, WPW, W-wa, 1995, s. 115-120.
• Sankowski D., Strzecha K., Kołodziejski H., Albrecht A., Wojciechowski R., 2003: Application of real-time operating system QNX for automatic determination of dynamic properties of resistance furnaces identification. Proc. IEEE Instrumentation and Measurement Technology Conference IMTC"03, Vail USA, pp. 1617-1621.
• Sardy S., Ibrahim L., Yasuda Y., 1993: An application of vision system for the identification and defect detection on woven fabrics by using artificial neural networks. Proc. Int. Joint Conf. Neural Networks, pp. 2141 -2144.
• Sastry P.S., Santharam G., Unnikrishnan K.P., 1994: Memory Neuron Networks for Identification and Control of Dynamical Systems. IEEE Trans. on Neural Networks, vol. 5, no. 2, pp. 178-184.
• Schoukens J., Guillaume P., Pintelon R., 1991: Design of Multisine Excitations. Proc. IEE Int. Conf. CONTROL 91, no. 332, vol. 1, pp. 638-643.
• Schoukens J., Nemeth J.G., Crama P., Rolain Y., Pintelon R., 2003: Fast approximate identification of nonlinear systems. Automatica, vol. 39, pp. 1267-1274.
• Sette S., Boullart L., Kiekens P., 1995: Self-Organizing Neural Nets: A New Approach to Quality in Textiles. Textile Res. J., vol. 65, no. 4, pp. 196-202.
• Sette S., Boullart L., Van Langenhove L. Kiekens P., 1997: Optimizing the Fiber-to-Yarn Production Process with a Combined Neural Network / Genetic Algorithm Approach. Textile Res. J., vol. 67, no. 2, pp. 84-92.
• Sette S., Boullart L., Van Langenhove L., 2000: Building a Rule Set for the Fiber-to-Yarn Production Process by Means of Soft Computing Techniques. Textile Res. J., vol. 70, no. 05, pp. 375-386.
• Shao H., 2008: Delay-Dependent Stability for Recurrent Neural Networks With Time-Varying Delays. IEEE Trans. Neural Networks, vol. 19, no. 9, Sept., pp. 1647-1651.
• Shen K., Lu J., Li Z., Liu G., 2005: An adaptive fuzzy approach for the incineration temperature control process. Fuel, vol. 84, pp. 1144-1150.
• Silva P.V.M., Seixas J.M., J. Seixas, 2001: A Hybrid Training Method for Neural Energy Estimation in Calorimetry. In: Bhat P. C., Kasemann M. (Eds): Advanced Computing and Analysis Techniques in Physics Research: VII Int. Workshop. American Inst. of Physics, pp. 86-88.
• Singh A. P., Kamal T. S., Kumar S., 2004: Virtual compensator for correcting the disturbing variable effect in transducers. Sensors and Actuators A, vol. 116, pp. 1-9.
• Singh A. P., Kamal T. S., Kumar S., 2005: Development of a virtual curve tracer for estimation of transducer characteristics under the influence of a disturbing variable. Sensors and Actuators A, vol 120, pp. 518-526.
• Singh A. P., Kamal T. S., Kumar S., 2006: Development of a virtual linearizer for correcting transducer static nonlinearity. ISA Transactions, vol. 45, no.3, pp. 319-328.
• Sokołowski J., Żochowski A., 1999a: On topological derivative method in shape optimization. SIAM Journal on Control and optimization, vol. 37, no. 4, pp. 1251-1272.
• Sokołowski J., Żochowski A., 1999b: Topological derivatives for elliptic problems. Inverse Problems, vol. 15, no. l, pp. 123-134.
• Södersröm T., Stoica P., 1997: Identyfikacja systemów. PWN, W-wa.
• Steinhart I.S., Hart S.R., 1968: Calibration curves for thermistors. Deep Sea Research 15 p. 497.
• Stępiński L., 2001: Komputerowe metody identyfikacji czujników termometrycznych. Praca dyplomowa magisterska, PL, Łódź.
• Strobel H., 1967: On the limits imposed by random noise and measurement errors upon system identification in the time domain. IF AC Symp. Prague.
• Strumiłło P., 2002: Modelowanie i analiza sygnału elektrokardiograficznego z zastosowaniem układów i przekształceń nieliniowych. Rozprawa habilitacyjna, Zeszyty Naukowe, Politechnika Łódzka, 906, Łódź.
• Sydenham P. i inni, 1998: Podręcznik metrologii. WKiŁ, W-wa.
• Szabatin J., 2000: Podstawy teorii sygnałów. WKiŁ, W-wa.
• Szydłowski H., 1981: Teoria pomiarów, WNT, W-wa.
• Tadeusiewicz R., 1991: Problemy biocybernetyki. PWN, Warszawa.
• Tadeusiewicz R., 1993: Sieci neuronowe. Akademicka Oficyna Wydawnicza, Warszawa.
• Tadeusiewicz R., Ogiela M.R., 2004: Medical Image Understanding Technology. Springer-Verlag, Berlin Heidelberg.
• Tadeusiewicz R., Gąciarz T., Borowik B., Leper B., 2007: Odkrywanie właściwości sieci neuronowych przy użyciu programów w języku C#, Wydawnictwo Polskiej Akademii Umiejętności, Kraków.
• Taylor H.R., Navarro H.A., 1983: A method to determine and reduce the response time of resistance thermometers under practical conditions. J. Phys. E: Sci. Instrum., vol. 16, pp. 916-918.
• Taylor H.R., Navarro H.A., 1987: A method to determine and compensate for the frequency response of a platinum resistance thermometer under practical conditions. 3rd Int. Conf. IMECO'87: Thermal Temp. Meas. Sc. Ind., Sheffield, pp. 137-147.
• Teeter J., Chow M. Y., 1998: Application of functional link neural network to HVAC thermal dynamic system identification. IEEE Trans. Ind. Electron., vol. 45, Feb. pp. 170-176.
• Tulleken H.J.A.F., 1988: A Generalized Binary Noise Test-Signal concept for improved identification experiment design. IFAC Identification and System Parameter Estimation. Beijing, PRC, pp. 577-585.
• Urbański M., 2000: Moduł programowy do linearyzacji charakterystyk statycznych wybranych czujników do pomiaru wielkości fizycznych. Praca dyplomowa magisterska, PŁ, Łódź.
• Vangheluwe L., Sette S., Pynckels F., 1993: Assessment of Set Marks by means of Neural Nets. Textile Res. J., vol. 63, no. 4.
• Vapnik V. N., 1982: Estimation of Dependences Based on Empirical Data. Springer Verlag, New York.
• Vapnik V. N., 1992: Principle of risk minimization for learning theory. Advances in Neural Information Processing Systems, Morgan Kaufmann, San Mateo CA, vol. 4, pp. 831-838.
• Vapnik V. N., Chervonenkis A., 1971: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, vol. 16, pp. 264-280.
• Veil D., Batista de Sousa P., Walfhorst B. 1998: Application of a neural network in the false-twist texturing process. Chemical Fibres International, vol. 48, no. 2, p. 155-156.
• Vidal M., Massicotte D. 1999: A VLSI Parallel Architecture of a Piecewise Linear Neural Network for Nonlinear Channel Equalization, in V. Piuri and M. Savino (eds.), Proc. 16th IEEE Instrum. and Meas. Tech. Conf. IMTC'99, Venice, Italy, May 24-26, pp. 1629-1634.
• Vollmer M., Göttsche F.M., Olesen F.S., 2000: Correction of the atmospheric influence on IR measurements by satellite with neural networks. Proc. 2000 EUMETSAT Meteorological Satellite Data Users' Conf., Bologna, Italy, pp. 506-511.
• Werbos, P. J., 1974: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Department of Applied Mathematics, Harvard University.
• Werbos, Paul J., 1988: Generalisation of Back Propagation with Application to a Recurrent Gas Market Model. Neural Networks, vol. 1, October, pp. 339-356.
• Wiatr K., Chwiej P., 2006: Implementacja sieci neuronowych w układach programowalnych FPGA dla potrzeb przetwarzania obrazów w czasie rzeczywistym, Kwartalnik Elektroniki i Telekomunikacji PAN, t. 52, z. 1, Warszawa, s. 115-128.
• Widrow B., 1988: DARPA Neural Network Study. Fairfax, VA: Armed Forces Communications and Electronics Assoc. Int. Press.
• Widrow B., Hoff M. E., 1960: Adaptive switching circuits. 1960 IRE Western Electric Show Conv. Rec., pt. 4, Aug., pp. 96-104.
• Widrow B., Plett G. L., 1996: Adaptive Inverse Control based on Linear and Nonlinear Adaptive Filtering. Proc. Int. Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, pp. 30-38.
• Widrow B., Stearns S., 1985: Adaptive signal processing. Prentice Hall,
• Wiśniewski S., 1979: Wymiana ciepła. PWN, Warszawa.
• Wiśniewski S., 1983: Pomiary temperatury w badaniach silników i urządzeń cieplnych. WNT, W-wa.
• Yager R., Filev D., 1995: Podstawy modelowania i sterowania rozmytego. WNT, W-wa.
• Yang H., Ni J., 2005: Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. Int. Journal of Machine Tools & Manufacture, vol. 45, pp. 455-465.
• Zadeh L.A., 1962: From circuit theory to system theory. Proc. IRE vol. 60, pp. 856-865.
• Zadeh L.A., 1965: Fuzzy sets. Information and Control, vol. 8, pp. 338-353.
• Zalewski A., Cegiełła R., 2002: Matlab - obliczenia numeryczne i ich zastosowania. Wydawnictwo NAKOM, Poznań.
• Zamarreno J.M., Vega P., 1999: Neural predictive control. Application to a highly non-linear system. Engineering Applications of Artificial Intelligence, vol. 12, pp. 149-158.
• Zbiciński I., Kaminski W., Ciesielski K., Strumiłło P., 1997: Dynamic and Hybrid Neural Model of Thermal Drying in a Fluidized Bed, Drying Technology, vol. 15 (6-8), pp. 1743-1752.
• Zieliński T., 2005: Cyfrowe przetwarzanie sygnałów. Od teorii do zastosowan. WKiŁ, W-wa.