Parameter identification of Chen and Lue systems: A differential evolution approach

This paper presents a novel differential evolution (DE) algorithm to solve the identification problem for Chen and Lue systems. A collection of unknown system parameters of Chen and Lue systems is taken as a manipulated parameter vector, and will be optimally evolved to approximate the actual parameters by using the proposed DE algorithm. Basically, the DE algorithm comprises three main operators of mutation, crossover, and selection to achieve the evolution. In the meanwhile, a cost function defined for solving the identification problem of chaotic systems is also minimized. Two illustrative examples are given to show the validity of the proposed method