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中国物理学会期刊

基于径向基函数神经网络的未知模型混沌系统控制

CSTR: 32037.14.aps.52.531

Control of chaos solely based on RBF neural network without an analytical model

CSTR: 32037.14.aps.52.531
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  • 基于径向基函数神经网络的智能方法对混沌进行控制-该方法不需要被控混沌系统的解析模型,控制的目标可以为周期轨道,也可以为连续变化的目标函数,在模型参数发生摄动和存在测量噪声情况下,控制仍然有效-研究了神经网络误差对控制精度的影响,并给出相关的定理及证明-针对Logistic映射和Henon吸引子的仿真结果,表明了此方法的有效性和可行性-

     

    An intelligent control method based on RBF neural network is proposed for chaos control- The control objective can be either periodic orbits or continuous variable functions without the need of an analytic model- The method is still effective when there are parameter perturbation and measurement noise- The influence of the RBF model error upon control precision is studied, and related theorem is developed and testified- Simulation results with a Logistic mapping and Henon attractor show the effectiveness and feasibility of this method-

     

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