基于PSO优化LSSVM的未知模型混沌系统控制

由于混沌系统存在非线性、不确定性等特点, 常规的控制方法难以获得满意的结果. 提出一种基于PSO优化LSSVM模型参数的混沌系统控制方法. 该方法利用PSO算法的收敛速度快和全局收敛能力, 优化LSSVM模型的惩罚因子和核函数参数, 避免了人为选择参数的盲目性, 提高了LSSVM模型的预测精度. 另外, 该方法不需要被控混沌系统的解析模型, 且当测量噪声存在情况下控制仍然有效. 仿真实验结果表明了该方法的有效性和可行性.关键词：混沌系统控制粒子群算法最小二乘支持向量机

Control of chaos solely based on PSO-LSSVM without usiing an analytical model

For a chaotic system with nonlinear ity and uncertainty, it is difficult to obtain the satisfactory performance using general control methods. A least square support vector marchine (LSSVM) control method based on particle swarm optimigation(PSO), is proposed for chaos control. Optimizing two parameters of LSSVM model by PSO abilities of the fast convergence and whole optimization, thus aroiding the blindness of man-made choice, the LSSVM-PSO model can enhance the capability of forecasting. The proposed method does not need any analytic model, and it is still effective in the presence of measurement noises. Simulation results with a Logistic mapping and Henon attractor show the effectiveness and feasibility of this method.