The Applications Of Fuzzy Neural Network Based On Genetic Algorithms

As three information sciences in computational intelligence, fuzzy Logic, neural networks and genetic algorithms are three important basic tools in intelligent control. Many research workers have concerned on the integration of techniques in these three sciences recently. This paper's work focus on the application of the integration in two ways.On the one hand, after studying the basic principles and theories of genetic algorithm, and studying the base and way of the integration of fuzzy logic and neural networks, the structure of connectionist fuzzy neural network is studied at some length. Around this structure, an optimization strategy with three steps is presented firstly. Based on the initial parameters that have been determined in the first step, the structure of a fuzzy neural network is optimized by using a genetic algorithm with the decimal coding scheme, and the parameters are optimized with the binary coding scheme. Numerical simulations show that the optimization strategy mentioned above is available and easy to use.Though the optimization strategy mentioned above use experiment data from expert to optimize the network structure and parameters, the performance of the network depends on the accuracy of expert experience to a great extent. In order to compensate and eliminate the influence of manmade factors, a fuzzy neural network controller based on the optimization of defuzzification is presented in this paper again. After the fuzzy neural network is got through using the strategy mentioned above, the defuzzification part of it is reconstructed, and the membership functions of the control action is further refined. Then the performance of the controller is optimized. The results of computer simulation...