基于量子粒子群算法的混沌系统参数辨识

针对混沌系统参数辨识问题, 在基本群智能算法粒子群优化算法的基础上, 提出量子粒子群算法, 测试函数证明了算法具有良好的全局优化能力. 进而将其应用于混沌系统参数辨识问题, 将参数辨识问题转化为多维函数空间上的优化问题. 通过对平衡板热对流典型混沌系统Lorenz系统进行研究, 并与基本算法和遗传算法比较. 仿真实验证明, 算法的有效性, 对混沌理论的发展有着非常重要的意义. 关键词： 量子粒子群算法 混沌系统 系统辨识

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.
Keywords： quantum particle swarm optimization chaotic system system identification