Czasopismo
2007
|
Vol. 14, No. 4
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715-727
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
The focus of this paper is on the problems of system identification, process modeling and time series forecasting which can be met during the use of locally recurrent neural networks in heuristic modeling technique. However, the main interest of this paper is to survey the properties of the dynamic neural processor which is developed by the author. Moreover, a comparative study of selected recurrent neural architectures in modeling tasks is given. The results of experiments showed that some processes tend to be chaotic and in some cases it is reasonable to use soft computing models for fault diagnosis and control.
Rocznik
Tom
Strony
715-727
Opis fizyczny
Bibliogr. 34 poz., rys.,tab., wykr.
Twórcy
autor
- Silesian University of Technology Politechnika Śląska], Department of Fundamentals of Machinery Design, ul. Konarskiego 22, 44-100 Gliwice
Bibliografia
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- [9] P.S. Jarvis, I.D. Wilson, S.E. Kemp. The application of a new attribute selection technique to the forecasting of housing value using dependence modelling. Neural Computing and Applications, 15(2): 136-153, April 2006.
- [10] A.J. Jones. New tools in non-linear modelling and prediction. Computational Management Science, 1(2): 109-149, July 2004.
- [11] A.J. Jones, D. Evans, S.E. Kemp. A note on the Gamma test analysis of noisy input/output data and noisy time series. Physica D: Nonlinear Phenomena, 229(1): 1-8, 2007.
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- [13] J. Korbicz. Robust fault detection using analytical and soft computing methods. Bulletin of the Polish Academy of Sciences: Technical Sciences, 54(1): 75-88, 2006.
- [14] J. Korbicz, J.M. Kościelny, Z. Kowalczuk, W. Cholewa. Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin-Heidelberg, 2004.
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- [24] R.B.C. Prudencio, T.B. Ludermir. Neural network hybrid learning: Genetic algorithms and Levenberg-Marąuardt. CiteSeer. IST - Copyright Penn State and NEC, 2001.
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- [26] M.T. Rosenstein, J.J. Collins, C.J. De Luca. A practical method for calculating largest Lyapunov exponents from smali data sets. Physica D, 65: 117-134, 1993.
- [27] M.T. Rosenstein, J.J. Collins, C.J. De Luca. Reconstruction expansion as a geometry-based framework for choosing proper delay times. Physica D, 73: 82-98, 1994.
- [28] M. Schlang, E. Broese, B. Feldkeller, O. Gramckow, M. Jansen, T. Poppe, C. Schaeffner, G. Soergel. Neural networks for process control in steel manufacturing. In International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 155-158. IEEE, 1997.
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- [30] R. Tadeusiewicz. Sieci neuronowe jako wciąż jeszcze nie w pełni wykorzystane narzędzie diagnostyki. In Pro-ceedings of II International Congress of Technical Diagnostics, vol. 1, pp. 81-94, Warsaw, 2000.
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- [33]L. Tsungnan, CL. Giles, G. Bill, S.Y. Kung. A delay damage model selection algorithm for NARX neural networks. IEEE, 1999.
- [34] L.A. Zadeh. Fuzzy logie, neural networks and soft computing. Communications of the ACM, 37(3), March 1994.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB1-0031-0019