Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method

An important issue in nonlinear science is parameter estimation for Lorenz chaotic systems. There has been increasing interest in this issue in various research fields, and it could essentially be formulated as a multidimensional optimization problem. A novel evolutionary computation algorithm, nonlinear time-varying evolution particle swarm optimization (NTVEPSO), is employed to estimate these parameters. In the NTVEPSO method, the nonlinear time-varying evolution functions are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates tothe full factorial experiments. The NTVEPSO method and other PSO methods are then applied to identify the Lorenz chaotic system. Simulation results demonstrate the feasibility and superiority of the proposed NTVEPSO method.