Parameter identification of chaotic systems using artificial raindrop algorithm

The knowledge about parameter identification is an important issue for the synchronization and control of chaos. These chaotic systems involve some input parameters which may greatly affect their dynamic behavior. In this paper, a newly developed meta-heuristic method - artificial raindrop algorithm (ARA) inspired from the phenomenon of natural rainfall, is applied to identify the unknown parameters of chaotic system first time in the literature. In order to verify the effectiveness of ARA, numerical experiments are carried on Chen, Lu¨, Ro¨ssler, Lorenz system, Logistic with time-delay system, and Mackey-Glass with time-delay system. The simulation results indicate that ARA can more effectively and accurately identify the parameters for given chaotic systems with respect to other seven state-of-the-art intelligent optimization algorithms.