Tuning certainty factor and local weight of fuzzy production rules by using fuzzy neural network
Eric C.C. Tsang, J.W.T. Lee, D.S. Yeung · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2002
Approximate reasoning in a fuzzy system is concerned with inferring an approximate conclusion from fuzzy and vague inputs. There are many ways in which different forms of conclusions can be drawn. Fuzzy sets are usually represented by fuzzy membership functions. These membership functions are assumed to have a clearly defined base. For other fuzzy sets such as intelligent, smart, or beautiful, etc., it would be difficult to define clearly its base because its base may consist of several other fuzzy sets or unclear nonfuzzy bases. A method to handle this kind of fuzzy set is proposed. A fuzzy neural network (FNN) is also proposed to tune knowledge representation parameters (KRPs). The contributions are that we are able to handle a broader range of fuzzy sets and build more powerful fuzzy systems so that the conclusions drawn are more meaningful, reliable, and accurate. An experiment is presented to demonstrate how our method works.