Parameter identification in chaotic systems by means of quantum particle swarm optimization

Aiming at the parameter identification problem in chaotic systems, we propose the quantum particle swarm optimization algorithm based on the swarm intelligence particle swarm optimization. The test functions show that the method has good global optimization. Then the method is applied to the parameter identification problem of the chaotic system. We transform the parameter identification problem into the optimization in the multi-dimensional function space. Through research on the balance board thermal convection in a typical chaotic Lorenz system, the proposed method has been compared with the basic algorithm and the genetic algorithm. Simulation results show that the proposed algorithm is effective, and is very important to the development of chaos theory.