用RBF网络控制非线性系统的混沌运动

Using RBF neural networks for controlling nonlinear chaos

设计 RBF前向神经网络 ,以 Ott、Grebogi和 Yorke混沌控制策略作为训练网络控制算法的基础 ,通过参数扰动模型输出数据训练网络来产生控制非线性系统的混沌运动必须的小扰动时间序列信号 ,使其成为混沌控制器 ,将嵌入在混沌吸引子中不稳定周期轨道镇定到稳定不动点 .Hénon映射的数值仿真结果证明该方法十分有效 .图 6 ,参

A RBF feed-forward neural network is devised and trained to achieve and maintain control of the unstable periodic orbits embeded in a chaotic attractor on the outputs of parameter perturbation model,also to generate small disturbance time series signals necessary for suppression nonlinear chaotic motion as chaos controller.The controlling algorithm used for training the network is based on the chaos control scheme developed by Ott,Grebogi and Yorke.The numerical simulations on Hénon map demonstrate the effectiveness of the proposed approach.6figs.,7refs.