Fuzzy Wavelet Neural Network Control System Applications

Many complicated systems can not be defined by conventional methods which haven't been built effective mathematic model and control method. Recently, both fuzzy and neural network technology has become a very active branch in intelligence control theory, wavelet analysis technology with local performance of time-frequency and multiresolution function has been rapidly developed in the past several years. So, based on the combination of fuzzy logical technique,wavelet analysis and neural network, this paper researches a new model, called fuzzy wavelet neural network. The main research works in this article are as follows:First, it offers an overview of the methods of identification and control for nonline ar systems and their current situation,and a survey to research progress on the combina tion of fuzzy logical technique,wavelet analysis and neural network at present is made.Second, dased on the combination of T-S fuzzy model and the feedforward neural networks, this paper researches a new model, called fuzzy neural network, and research a mixed learning algorithm based on the combination of gradient descent algorithm andα? LMS algorithm. Here, The nonlinear parameters are learned by gradient descent al- gorithm and linear parameters,viz.,weights are learned byα? LMS algorithm.The app roximation capabilities of the model to nonlinear function are theoretically proved.Finally,by utilizing two important properties,viz.,multi-resolution and compression of wavelets in conjunction with fuzzy neural networks, two new fuzzy wavelet neural networks are proposed here for non-linear system identification. The simulations to the static system demonstrate that the proposed two fuzzy wavelet neural networks are optimal.