基于改进粒子群优化算法的混沌系统参数估计方法

Parameter estimation for chaotic system based on particle swarm optimization

估计混沌系统的未知参数是混沌控制与同步中必须解决的关键问题.利用群集智能的新进展粒子群优化算法(PSO)的全局搜索能力,从初始粒子群的产生、目标函数的处理的角度改进PSO,将改进的PSO引入混沌系统参数估计和在线估计.仿真试验表明,改进算法具有良好的适应性、较高的收敛可靠性及精度,对信号叠加噪声的情形也具有较高的鲁棒性,是混沌系统参数估计的一种成功算法.

It＇s of vital importance to estimate the unknown parameters of chaos systems in chaos control and synchronization. We firstly improve the newly developed particle swarm optimization （PSO） in view of the population initialization and objective function treatment. Then we use the improved algorithms for parameter estimation and on-line estimation of chaotic system for its global searching ability. Experiments show that the improved method has better adaptability, reliability and high precision is robust to noise. It is proved to be a successful approach in parameter estimation for chaotic systems.