The Optimization And Application Study Of Fuzzy Neural Network

Fuzzy neural network(FNN) is an active branch in the intelligent control. FNN is composed of the neural network and fuzzy logic system. It is the organic integration of the two parts. It tries to make appropriate integration of logic reasoning technology and self-learning capability. FNN can deal with the abstract information, such as the language information. And it is good at self-learning and self-tuning.The developments of FNN and FNN theory are reviewed. The characters, learning methods and application in control fields are discussed in detail. In this paper, a variable-structure fuzzy neural network is designed to cope with the problem that the structure of a usual FNN is too complicated and a FNN is too difficult to be constructed. A hybrid algorithm is proposed according to the character of FNN structure, using the recursive least squared method to optimize the linear parameters and conjugate gradient algorithm to tune the nonlinear parameters. At the same time an adaptive tuning method is adopted to avoid the improper value of learning rate. This method greatly enhances the convergence speed and makes it possible for the industrial on-line application. According to growing criterion and pruning strategy of hidden units, the structural optimization scheme is developed for variable-structure fuzzy neural network.Based on simulation analyzing of PID self-tuning control and internal model control (IMC) theories, the identification and control methods for the complicated nonlinear dynamic systems are proposed. Then, the author applies IMC based on variable-structure fuzzy neural network methods in CSTR. To compare with PID control, IMC has the excellent effect.