Parameter estimation of fractional-order chaotic system based on adaptive artificial evolutionary fish swarm algorithm

In this paper, we study challenges of estimating parameters in fractional-order chaotic systems. By establishing an identification model for such systems, the parameter estimation process is transformed into an optimization task. The artificial fish swarm algorithm (AFSA) is employed for identifying the derived model. To enhance the convergence speed and prevent falling into local optimum, the adaptive vision and step are applied, and the evolutionary strategy is introduced into the fish swarm, then developing the adaptive artificial evolutionary fish swarm (AAEFSA) method. At last, the effectiveness of the proposed approach is validated through numerical simulations on the Lorenz fractional-order chaotic system. Experimental results reveal that the proposed AAEFSA algorithm has good estimation accuracy for the parameter estimation of fractional-order chaotic systems.