TYPE-2 FUZZY NEURAL NETWORK SYSTEMS AND LEARNING

This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning using genetic algorithm. The so-called type-1 fuzzy neural network (FNN) has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. And, the membership functions (MFs) and the rules can be designed and trained from linguistic information and numeric data. However, there is uncertainty associated with information or data. Therefore, the type-2 fuzzy sets are used to treat it. Type-2 fuzzy sets let us model and minimizes the effects of uncertainties in rule-base fuzzy logic systems (FLS). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by real-code genetic algorithm. Copyright c ©2002 Yang’s Scientific Research Institute, LLC. All rights reserved.

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