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A study is presented on the application of particle swarm optimization (PSO) for estimation of parameters in chaotic systems. The parameter estimation is formulated as a nonlinear optimization problem using PSO to minimize the synchronization error for the observable states of the actual system and its mathematical model. The procedure is illustrated using a typical chaotic system of Lorenz equations. The effectiveness of different variants of PSO on parameter estimation is studied with a wide search range of parameters. The results show the capability of the proposed PSO based approach in estimating the chaotic system parameters even in the presence of observation noise.
Swarm Intelligence Symposium, 2009. SIS '09. IEEE
Date of Conference: March 30 2009-April 2 2009