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Artificial Neural Networks - Modern Systems for Safety Control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A short review of the applications of artificial neural networks in different fields of industry with a description of their main properties is made. Such systems have specific properties typical for the human brain, which can decide on the superiority of artificial neural networks over standard control systems. Basic types of such networks as well as their principles of operation and successful applications are described. The application of artificial neural networks in safety engineering is discussed with stress on their special properties, which are necessary in safety critical systems.
Rocznik
Strony
317--332
Opis fizyczny
Bibliogr. 33 poz., rys.
Twórcy
  • Central Institute for Labour Protection, Poland
  • Central Institute for Labour Protection, Poland
Bibliografia
  • 1.Burgess, A.N. (1995). Non-linear model identification and statistical significance tests and their application to financial modelling. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 312-317). London: Institution of Electrical Engineering.
  • 2.Chua, L.O., & Yang, L. (1988). Cellular neural networks. IEEE Transactions on Circuits and Systems, 35, 1257-1274.
  • 3.Corcoran, P., & Lowery, P. (1995). Neural processing in an electronic odour sensing system. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 415-420). London: Institution of Electrical Engineering.
  • 4.Draghid, S. (1996). Security applications of a neural networks based artificial vision systems. In A.B. Bulsari, S. Kallio, & D. Tsaptsinos (Eds.), Proceedings of International Conference on Solving Engineering Applications of Neural Networks - EANN 96, London (pp. 645-648). Turku, Finland: Systems Engineering Association.
  • 5.Dumpelmann D.E., & Eiger, C.E. (1996). Separating EEG spike-clusters in epilepsy by growing and splitting net. In Proceedings of International Conference on Artificial Neural Networks - ICANN 96, Bochum, Germany (pp. 239-245). Berlin, Germany: Springer.
  • 6.Hertz, J., Krogh, A., & Palmer, R.G. (1995). Introduction to the theory of neural computations. Warsaw, Poland: Wydawnictwa Naukowo-Techniczne.
  • 7.International Organization for Standardization. (1994). Quality systems - model for quality assurance in design, development, production, installation and servicing. (Standard No. ISO 9001:1994). Geneva, Switzerland: Author.
  • 8.Jarvinen, J., & Karwowski, W. (1992). Modeling of man-machine interactions in FMS using artificial neural network tools. In P. Brodner & W. Karwowski (Eds.), Ergonomics and automated systems (pp. 343-348). New York: Elsevier Science.
  • 9.Johnson, J.H., Picto, P.D., & Hallam, N.J. (1993). Safety critical neural computing. Artificial Intelligence in Engineering, 8, 307-313.
  • 10.Kacprzyk, T., & Slot, K. (1995). Komorkowe sieci neuronowe [Cellular neural networks]. Warsaw, Poland: Państwowe Wydawnictwo Naukowe.
  • 11.Kaiser, M., & Wallner, F. (1996, May). Safe design o f neural networks for monitoring, diagnosis, and control. Theory and applications. Paper presented at the Polish-German Seminar on Artificial Neural Networks in Safety Engineering, Warsaw, Poland.
  • 12.Kirkpatrick, S., Gelatt, C.D., Jr., & Vecci, M.P. (1983). Optimization by simulated annealing. Science, 220, 671-679.
  • 13.Kolanoski, H. (1996). Application of artificial neural networks in particle physics. In Proceedings o f International Conference on Artificial Neural Networks - ICANN 96, Bochum, Germany (pp. 227-233). Berlin, Germany: Springer.
  • 14.Kosiński, R. (1996). Assessment of the possibilities of applying Artificial Neural Networks in safety engineering and numerical investigations of a chosen model of a neural network applicable to the analysis of safety at work stands (Project No. IV-026). Warsaw, Poland: Central Institute for Labour Protection. (In Polish).
  • 15.Kuivanen, R. (1995). Methodology for simultaneous robot system safety design (VTT Publications 219). Espoo, Finland: VTT.
  • 16.Laaksonen, J., & Oja, E. (1996). Subspace dimension selection and averaged learning subspace method in handwritten digit classification. In Proceedings of International Conference on Artificial Neural Networks - ICANN 96, Bochum, Germany (pp. 227-233). Berlin, Germany: Springer.
  • 17.Leary, P., Gallinari, P., & Didelet, E. (1996). Diagnosis tools for telecommunication network traffic management. In Proceedings of International Conference on Artificial Neural Networks - ICANN 96, Bochum, Germany (pp. 209-215). Berlin, Germany: Springer.
  • 18.Lee, D.H , Payne, J.S., Byunn, H.G., & Persaud, K.C. (1996). Application of radial basis function neural networks to odour sensing. In Proceedings of International Conference on Artificial Neural Networks - ICANN 96, Bochum, Germany (pp. 299-305). Berlin, Germany: Springer.
  • 19.Leonard, J.J., & Durrant-Whyte, H .F. (1992). Direct sonar sensing for mobile robot navigation. Boston, MA: Kluwer Academic Press.
  • 20.Lin, Q., & Kuo, C. (1997). A Virtual reality interface for navigation of unmanned underwater vehicles. In P. Seppala, T. Luopajarvi, C. Nygard, & M. Mattila (Eds.), Proceedings of 13th Triennial Congress of International Ergonomics Association, Tampere, Finland (Vol. 3, pp. 49-51). Helsinki, Finland: Finnish Institute of Occupational Health.
  • 21.Luttrell, S.P. (1995). Using self-organizing maps to classify radar range profiles. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 335-341). London: Institution of Electrical Engineering.
  • 22.Lynch, M.R ., & Haunt, R.G. (1995). Applications of linear weight neural networks for visual image reconstruction. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 127-133). London: Institution of Electrical Engineering.
  • 23.Manvarig, H.S. (1995). The use of an artificial neural network to improve precision in trace level, quantitative analysis of heavy metal. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 375-380). London: Institution of Electrical Engineering.
  • 24.Molga, E. (1996). Applications of neural networks to modeling, recognition and assessment of technological installations in the chemical industry. Paper presented at the VII Engineering of Chemical Reactors Symposium, Ustron, Poland.
  • 25.Morgan, G., & Austin, J. (1995). Safety critical neural networks. In Proceedings of International Conference on Artificial Neural Networks, Cambridge, UK (pp. 212-217). London: Institution of Electrical Engineering.
  • 26.Muller, B., Reinhardt, J., & Strickland, M.T. (1995). Neural networks. Berlin, Germany: Springer.
  • 27.Nelson, M. McCord, & Illingworth, W.T. (1994). A practical guide to neural nets. Reading, MA: Addison-Wesley.
  • 28.Osowski, H. (1996). Sieci neuronowe w ujęciu algorytmicznym [An algorithmic approach to neural networks]. Warsaw, Poland: Wydawnictwa Naukowo-Techniczne.
  • 29.Patterson, D.W. (1996). Artificial Neural Networks. New York: Prentice Hall.
  • 30.Ritter, H., Martinez, T., & Schulten, K. (1992). Neural computation and self-organizing maps. London: Addison-Wesley.
  • 31.Rodd, M.G. (1995, October). Engineering real-time systems. Computational and Control Engineering Journal, 233-240.
  • 32.Yang, X., Yang, T., & Yang, L.B. (1994). Extracting focused object from defocused background using cellular neural networks. In CNNA 94 - Third IEEE International Workshop on Cellular Neural Networks and their Applications, Rome, Italy (pp. 451-455). Rome, Italy: La Sapienza University.
  • 33.Zarandy, A., Werblin, F., Roska, T., & Chua, L.O. (1994). Novel types of analogic CNN algorithm for recognizing bank-notes. In CNNA 94 - Third IEEE International Workshop on Cellular Neural Networks and their Applications, Rome, Italy (pp. 273-278). Rome, Italy: La Sapienza University.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d07b77fc-b558-413e-ae18-8379ef414049
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