The present work deals with application of recently developed evolutionary computing based training methods for non-linear system identification problem. Generally, most of the systems are nonlinear in nature. The conventionally used standard derivative based identification scheme does not work satisfactorily for nonlinear systems, which is due to premature settling of the model parameters. To prevent the premature settling of the weights, evolutionary computing based update algorithms have been proposed. In this paper we have compared three popular derivative free evolutionary computing based update algorithms namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) for identification of nonlinear systems, in terms of convergence graph of cost function over number of iterations. It has been demonstrated that the derivative free population based schemes provided excellent performance for identification of nonlinear systems and they are not trapped in problem of local minima as well.

1. D. E. Goldberg, "Genetic algorithms in search, optimization and machine learning",AdditionWesley,1989.
2. A. E. Eiben and J. E. Smith, "Introduction to Evolutionary Computing", Springer, ISBN 3-540-40184-9, 2003.
3. R. Storn and K. Price, "Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces", Journal of Global Optimization, vol. 11, pp. 341-359, 1997.
4. J. Vesterstroem and R. Thomsen, "A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems," Proc. Congr. Evol. Comput. , vol. 2, pp. 1980–1987, 2004.
5. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives , Swagatam Das, Ajith Abraham, and Amit Konar.
6. Swagatam Das, Ajit Abraham & Amit Konar, "Particle Swarm Optimization and Differential algorithms: technical analysis, applications & hybridization perspectives" Advances in Computational intelligence in industrial systems, study in computational intelligence, 2008, Vol 116/2008, 1-38, DOI:10. 1007/978-3-540-78297-1-1
7. S. Chen, S. A. Billings and P. M. Grant, :Nonlinear system identification using neural networks", Int. J. Contr. , vol. 51, no. 6, pp. 1191-1214, June 1990.
8. J. Abonyi, J. Madar, L. Nagy, and F. Szeifert, "Interactive Evolutionary Computation in Identification of Dynamical Systems", Comp. & Chem. Engineering, Carnegie Mellon University, IF: 0. 784, 2004
9. Kumon, T. Iwasaki, M. Suzuki, T. Hashiyama, T. Matsui, N. Okuma, "Nonlinear system identification using Genetic algorithm" Industrial Electronics Society, IECON 2000. 26th Annual Conference of the IEEE- Volume 4, 22-28 Oct. 2000 Page(s):2485 - 2491 vol. 4, 2000.
10. S. Haykin, Adaptive Filter Theory. Upper Saddle River, NJ: Prentice-Hall Inc. , 1996.
11. N. P. Padhy, Artificial Intelligence and Intelligent Systems, chapter-9,10, pp-459-578, Oxford Publications, Second Edition
12. J. Sjoberg, Q. Zhang et. al. : Nonlinear black-box modeling in system identification: a unified overview, Automatica, Vol. 31, No. 12, pp. 1691- 1724, 1995.
13. T. A. Johansen: Multi-objective identification of FIR models, Proceedings of IFAC Symposium on System Identification SYSID2000, Vol. 3, pp. 917-922, 2000.
14. Hayakawa T, Haddad WM, Bailey JM, Hovakimyan N. Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems. IEEE Transactions on Neural Networks 2005; 16(2):387-398.

Nonlinear System Identification Particle Swarm Optimization Genetic Algorithm Differential Evolution