Parameter estimation of nonlinear systems in noisy environments using genetic algorithms

Traditional approaches for parameter estimation have difficulty with both nonlinear systems and with systems in noisy environments. In this paper we explore the use of genetic algorithms as the key search procedure of a methodology for estimating the parameters of discrete time nonlinear systems in noisy environments. Examples are presented to illustrate the effectiveness of the proposed approach.

[1]  M. Tummala,et al.  Iterative algorithm for identification of third order Volterra systems , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[2]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[3]  W. Sethares,et al.  Identification of a nonlinear system modeled by a sparse Volterra series , 1992, [Proceedings 1992] IEEE International Conference on Systems Engineering.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Guy A. Dumont,et al.  Genetic algorithms in system identification , 1988, Proceedings IEEE International Symposium on Intelligent Control 1988.

[6]  John J. Greffenstette,et al.  A System for Learning Control Strategies with Genetic Algorithms , 1989 .

[7]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[8]  Kenneth de Jong,et al.  Adaptive System Design: A Genetic Approach , 1980, IEEE Trans. Syst. Man Cybern..

[9]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..

[10]  M. J. Hicks,et al.  Recursive adaptive filter design using an adaptive genetic algorithm , 1982, ICASSP.