Parameter Estimation for Chaotic Systems Based on Chaotic-search Artificial Bee Colony Algorithm

摘要

Parameter estimation for chaotic systems is the key to chaotic systems' control and synchronization. In essence, it is a multi-parameter optimization problem. In order to accurately estimate the unknown parameters for chaotic systems, the Chaotic-search Artificial Bee Colony Algorithm (CSABC) is proposed to solve the optimization problem in this paper. By introducing chaotic sequences to reinitialize the individuals which have fallen into local optimum, this algorithm gets the neighborhood points of the local optimum through iteration and helps the constrained individuals quickly escape from the local optimum and find the global optimal solution, which implies a remarkable improvement in global searching ability and convergence velocity. Typical Lorenz Chaotic System is utilized for numerical simulation, the result shows that the CSABC algorithm can effectively estimate the unknown parameters.