Learning fuzzy rules through neural networks
Nikola Kirilov Kasabov · 2002
Presents a method for learning fuzzy rules through training a neural network with the backpropagation algorithm. Membership functions for the fuzzy concepts participating in the rules can also be learned through the proposed scheme. The learned fuzzy rules can then be implemented in a fuzzy inference machine, and a function which approximates the real goal function between the independent input variables and the dependent output variables can be derived. This approach has been compared with the regression analysis approach on the example of a simple forecasting problem. Both neural networks and fuzzy systems have shown superior accuracy. Mixing of the three approaches is also discussed.>