Particle swarm optimization for chaotic system parameter estimation

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.