Parameter Estimation of Loranz Chaotic Dynamic System Using Constriction factor approach in Particle Swarm Optimization (CFAPSO)

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Constriction factor approach in particle swarm optimization (CFAPSO) is a new member of meta-heuristics. This paper focuses on using the CFAPSO to solve this problem. Simulation results demonstrate the merit, effectiveness and robustness of CFAPSO Algorithm.