Abstract: A novel control method based on Niche Adaptive Differential Evolution Wavelet Neural Network（NADE-WNN） is proposed for uncertain chaotic system.The Wavelet Neural Network is used to study dynamic characters of uncertain chaotic system and control it.In order to raise learning accuracy and convergence rate，the structures and parameters of wavelet neural network are optimized by NADE algorithm at the same time in the model.The algorithm can get a best network structure and improve learning accuracy and the global convergence.The simulation results show that the NADE-WNN is still effective when there are external disturbance and parameter perturbation，and then the network structure，the control precision and convergence rate all outperform basic neural networks.

Key words: Niche Adaptive Differential Evolution（NADE） algorithm, Wavelet Neural Network（WNN）, parameter uncertainties, chaos control

摘要： 提出一种基于小生境自适应差分进化小波神经网络（NADE-WNN）的方法对不确定混沌系统进行控制。该方法利用小波神经网络学习未知模型混沌系统的动态特性并实施控制，为提高神经网络的学习精度和收敛速度，采用小生境自适应差分进化算法同时优化小波神经网络的结构和参数，简化网络结构，提高网络的学习精度和全局收敛性。仿真实验结果表明，在有外部干扰和参数摄动的情况下，NADE-WNN仍能对不确定混沌系统进行有效控制，且网络结构、控制精度和收敛速度都优于传统神经网络。

关键词: 小生境自适应差分进化算法, 小波神经网络, 参数不确定性, 混沌控制