Learning of weighted fuzzy production rules based on fuzzy neural network

In this paper, we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to refine or tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs by including local and global weights. By experimenting our method with some existing benchmark examples, the proposed method is found have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is greatly reduced.