Abstract
There exists no standard method for obtaining a nonlinear input-output model using external dynamic approach. In this work, we are using an evolutionary optimization method for estimating the parameters of an NFIR model using the Wiener model structure. Specifically we are using a Breeder Genetic Algorithm (BGA) with fuzzy recombination for performing the optimization work. We selected the BGA since it uses real parameters (it does not require any string coding), which can be manipulated directly by the recombination and mutation operators. For training the system we used amplitude modulated pseudo random binary signal (APRBS). The adaptive system was tested using sinusoidal signals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dahleh M. A., E. D. Sontag, D. N. C. Tse, and J. N. Tsitsiklis, 1995, “Worst-case identification of nonlinear fading memory systems”, Automatica, vol. 31, pp. 303–308. http://citeseer.ist.psu.edu/dahleh95worstcase.html
De Falco, I., Delia Cioppa, A., Natale, P., Tarantino, E. (1997), “Artificial Neural Networks Optimization by means of Evolutionary Algorithms”, http://citeseer.nj.nec.com/defalco97artificial.html
Deb Kalyanmoy (2002), “Multi-Objective Optimization using Evolutionary Algorithms”, John Wiley & Sons, LTD, New York, USA.
Gomez Juan and Baeyens Enrique (2004), “Identification of Multivariable Hammerstein Systems using Rational Orthonormal Bases”, http://citeseer.ist.psu.edu/421047.html.
Gómez Juan C., Enrique Baeyens (2004), “Identification of Nonlinear Systems using Orthonormal Bases”, http://citeseer.ist.psu.edu/596443.html
Guo Fen (2004), “A New Identification Method for Wiener and Hammerstein Systems”, Institut fur Angewandte Informatik, http://bibliothek.fzk.de/zb/berichte/FZKA6955.pdf
Ikonen E., Najim K. (1999), “Learning control and modelling of complex industrial processes, Overview report of our activities within the European Science Foundation’s programme on Control of Complex Systems (COSY) Theme 3: Learning control”. http://cc.oulu.fi/~iko/lccs.html
Keane Martin A., Koza John R., Rice James P. (1993). Finding an impulse response function using genetic programming. In Proceedings of the 1993 American Control Conference, volume 3, pages 2345–2350, San Francisco, CA, 2.-4. June 1993. IEEE, New York. http://citeseer.ist.psu.edu/keane93finding.html
Ljung Lennart (1999), “System Identification. Theory for the User. Second Edition”, Prentice Hall PTR, USA.
Nelles Oliver (2001), “Nonlinear System Identification. From Classical Approaches to Neural networks and Fuzzy Models”, Springer-Verlag Berlin Heidelberg. Germany. 2001. pp. 15, 457–511.
Montiel R. Oscar, Oscar Castillo, Roberto Sepilveda, Patricia Melin (2004a), “The evolutionary learning rule for system identification”, Applied Soft Computing Journal. Special issue: Soft Computing for Control of Non-Linear Dynamical Systems. Volume 3, Issue 4. December 2003. pp. 343–352
Montiel Oscar, Oscar Castillo, Patricia Melin, Roberto Sepilveda (2004b), “Asynchronous hybrid architecture for parametric system identification using fuzzy real coded evolutionary algorithm”, Nonlinear Studies, Volume 11, Number 1.
Mihlenbein Heinz, Dirk Schlierkamp-Voosen (1994), “The science of breeding and its application to the breeder genetic algorithm EGA”. Evolutionary Computation, 1(4):335–360.
Mühlenbein Heinz, Evolutionary Algorithms: Theory and Applications, http://citeseer.ist.psu.edu/110687.html.
Mihlenbein Heinz and Schilierkamp Voosen (1993), “Predictive Model for Breeder Genetic Algorithm”, Evolutionary Computation. 1(1): 25–49.
Narendra K. S. and P. G. Gallman (1996), “An iterative method for the identification of nonlinear systems using a Hammerstein model”. IEEE Transactions on Automatic Control, AC-11:546–550. July 1966.
Rodríguez Katya Vázquez, Fonseca Carlos M. Fleming Peter J. (1997). Multiobjective Genetic Programming: A Nonlinear System Identification Application. Late Breaking Papers at the Genetic Programming 1997 Conference, Editor John R. Koza, Standford Bookstore, USA, pp. 207–212.
Severance Frank L. (2001), “System Modeling and Simulation. An Introduction”, John Wiley & Sons Ltd., UK.
Sjoberg, J., Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P.-Y. Glorennec, H. Hjalmarsson and A. Juditsky (1995). “Nonlinear black-box modeling in system identification: a unified overview”. Automatica 31(12), 1691–1724. http://citeseer.nj.nec.com/sjoberg95nonlinear.html
Vijay K. Madisetti, Douglas B. Williams (1997). The Digital Signal Processing Handbook, A CRC Handbook Published in Cooperation with IEEE Press, pp. 15–1, 18–1, 18–12, 20–1, 20–4.
Jang J.-S.R., C.-T. Sung, E. Mizutani (1997), “Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence”. Prentice Hall. NJ, USA.
Voigt H.M., Mihlenbein, D. Cvetkovic (1995), “Fuzzy Recombination for the Breeder Genetic Algorithm”, Proceedings of the Sixth International Conference on Genetic Algorithms, published by Morgan Kaufmann, pp. 1104–111.
Winkler S., Affenzeller M., Wagner S. (2004), “Identifying Nonlinear Model Structures Using Genetic Programming Techniques”. Cybernetics and Systems 2004, pp. 689–694. Austrian Society for Cybernetic Studies, 2004.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Montiel, O., Castillo, O., Melin, P., Sepúlveda, R. (2006). Evolutionary Modeling Using A Wiener Model. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_47
Download citation
DOI: https://doi.org/10.1007/3-540-31662-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
eBook Packages: EngineeringEngineering (R0)