The Learning Algorithms Of Fuzzy Neural Network

Artificial Neural Network (ANN) is a nonnumeric algorithm based on the imitation of the structure of human brain. The hybridization of Fuzzy Mathematics (FM) and Artificial Neural Network is Fuzzy Neural Network (FNN). As a new technology in the field of artificial intelligence, FNN is an important method for information extraction, and it has a very large scale of application. In this paper, in according to a lot of new achievements about Fuzzy Mathematics and ANN, we use Self Organizing Algorithm and Genetic Algorithm (GA) as the learning methods of FNN, hope to explore new way of information extraction from complicated data. The main research work includes:In section one, we present the theory of Fuzzy Mathematics and its applications.In section two, we mainly discuss the basic algorithm of ANN. In the past several decades, ANN has been applied in many fields extensively, and lots of different algorithms for ANN are created. In this paper, we will adopt the fundamental frame of BP ANN to construct the network system.In section three, by the basic knowledge of Fuzzy Mathematics and ANN, we detailedly express the way of constructing the FNN which can reflect the information of fuzzy number. In the same time, we use Self Organizing Algorithm and GA to modify the structure and parameters of FNN, hope that the combination of these algorithms can be carried out in many kinds of complex informational problems, such as pattern recognition, quality evaluation, and data extraction and so on.