On the integration of neural networks and fuzzy logic systems

Both Neural Networks (NN) and Fuzzy Logic Systems (FLS) deal with important aspects of knowledge representation, inferencing, and learning process but they use different approaches and have their own strengths and weaknesses. NN can learn from sample data automatically, but lack of explanation ability. FLS are capable to perform approximate reasoning, but usually are not self-adaptive. The real power of artificial intelligence lies in the integration of NN and FLS. The existing methods of integration can be classified into three broad categories: (1) building FLS with NN, (2) converting NN into FLS, and (3) combining FLS and NN into a hybrid system. A variety of applications have been developed with the integration of NN and FLS. The direction of further research in this area is suggested.