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An Adaptive Fuzzy Neural Network Based on Self-Organizing Map (SOM)

Các GMM và PW mật độ cắm vào khuôn khổ kiểm tra giả thuyết để có được quyết định loại trừ liên quan đến dự toán. Các quy tắc quyết định lựa chọn tối ưu hóa Lagrangian chức năng. Cách tiếp cận dựa trên ranh giới được xác định trong cộng đồng SVM. Nó tránh việc lập dự toán mật độ đầy đủ các chức năng đó là không cần thiết vì mật độ duy nhất trong hàng xóm mui xe của biên giới cần phải biết chính xác | 1 An Adaptive Fuzzy Neural Network Based on Self-Organizing Map SOM Jun-fei Qiao and Hong-gui Han Beijing University of Technology China 1. Introduction This chapter shows a new method of fuzzy network which can change the structure by the systems. This method is based on the self-organizing mapping SOM Kohonen T. 1982 but this algorithm resolves the problem of the SOM which can t change the number of the network nodes. Then this new algorithm can change the number of fuzzy rules it takes the experienced rules out of the necessary side for the number of the fuzzy rules. We use this new algorithm to control the dissolved oxygenic in the wastewater treatment processes. This proposed algorithm can adjust subjection function on-line optimize control rules. The results of simulations show that the controller can take the dissolved oxygenic to achieve the presumed request and prove the superiority of this proposed algorithm in the practical applications. The research of the structure of the Neural Network is a hotspot currently. A neural network model with strong relations to the area of fuzzy systems is the fuzzy neural network model T.Poggio and F.Girosi 1990 . Based on the IF-THEN rules the fuzzy logic rules can be clustered. The functional equivalence of restricted fuzzy neural networks has been shown as Fig1 Linguistic variable 2 Linguistic variable 2 . a . b Fig. 1. Relations of the fuzzy linguistic before and after clustering algorithm 2 Self-Organizing Maps Fig a is a restricted fuzzy system which distributes the input into a number of fuzzy regions. Fig b is the relations of fuzzy linguistic after clustering. Fig a and Fig b show that the relations of fuzzy linguistic before and after clustering are not the same. In fact the functions of the fuzzy rules are also not the same in the general control. Especially in the fuzzy neural network Yu Zhao Huijun Gao Shaoshuai Mou 2008 every node represented a rule in rules layer. But some edge rules are even not be used in