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对于一类模型未知的非混沌系统采用模糊神经网络辨识其动力学特性 ,将得到的模糊神经网络辨识模型应用于逆系统方法中 ,实现了一类模型未知非混沌系统的混沌化控制 .该方法不依赖于被控对象的数学模型 ,就可以进行有效控制 .研究了模糊神经网络辨识误差对控制精度的影响 ,证明了适当设计参数可以使由辨识误差引起的控制误差小于辨识误差 .针对连续和离散两类系统的仿真研究证明了该方法的有效性 .
For a class of non-chaotic systems with unknown model, the fuzzy neural network is used to identify the dynamic characteristics of the non-chaotic system. The fuzzy neural network identification model is applied to the inverse system method to realize the chaotic control of a class of unknown non-chaotic systems. It can control effectively without depending on the mathematic model of the controlled object.It is proved that the control error caused by the identification error can be smaller than the identification error due to the proper design parameters.Finally, Simulation studies of two discrete systems demonstrate the effectiveness of the proposed method.