A Particle Swarm Optimization Approach for Parameter Identification of Lorenz Chaotic System

An important problem in engineering is the identification of nonlinear systems, among them chaotic systems have received particular attention due to their complex and unpredictable behaviors. In this paper, a Particle Swarm Optimization (PSO) technique is applied for online parameter identification of Lorenz chaotic system. The difficulties of online implementation mainly come from the unavoidable computational time to find a solution. Due to this, first an Improved Particle Swarm Optimization (IPSO) is proposed to increase the convergence speed and accuracy of the Standard Particle Swarm Optimization (SPSO) to save tremendous computation time. Second. IPSO is also improved to detect and determine the variation of parameters. Finally, a numerical example is given to verify the effectiveness of the proposed method compared to Genetic Algorithm (GA) and SPSO.