Brain-inspired STA for parameter estimation of fractional-order memristor-based chaotic systems

It is very important to estimate the unknown parameters of the fractional-order memristor-based chaotic systems (FOMCSs). In this study, a brain-inspired state transition algorithm (BISTA) is proposed to estimate the parameters of the FOMCSs. In order to generate a better initial population, a novel initialization approach based on opposition-based learning is presented. To balance the global search and local search, and accelerate the convergence speed, the mutual learning and selective learning are proposed in the optimization process. The performance of the proposed algorithm is comprehensively evaluated on two typical FOMCSs. The simulation results and statistical analysis have demonstrated the effectiveness of the proposed algorithm. For the fractional-order memristor-based Lorenz system, the proposed method can increase the estimated value of parameters by at least one order of magnitude compared with the other methods.