Parameter identification for fractional-order multi-scroll chaotic systems based on original dual-state transition algorithm

Abstract

Parameter estimation for fractional-order chaotic systems is a multi-dimensional optimization problem, which is one of the important issues in fractional-order chaotic control and synchronization. With the orthogonal learning strategies and the original dual learning mechanism, the original dual-state transition algorithm is proposed for solving the problem of parameter estimation in fractional-order chaotic systems. The orthogonal learning strategy is presented which can increase the diversity of initial population and improve the convergence ability. And the original dual learning mechanism is presented which can increase the space ability of states, and also can improve the search capability of the algorithm. In the process of identification, we adopt Radau IIA method to solve the fractional-order differential equation. The simulation of the fractional-order multi-scroll chaotic systems with or without noise is conducted and the results demonstrate the e?ectiveness, robustness, and versatility of the proposed algorithm.