Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8882
Title: Tuning certainty factor and local weight of fuzzy production rules by using fuzzy neural network
Authors: Tsang, ECC
Lee, JWT
Yeung, DS
Keywords: Certainty factors
Fuzzy neural networks (FNNs)
Fuzzy production rules
Local weights
Issue Date: 2002
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2002, v. 32, no. 1, p. 91-98 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: 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 nuclear nonfuzzy bases. In this paper, 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 meaninful, reliable, and accurate. An experiment is presented to demonstrate how our method works.
URI: http://hdl.handle.net/10397/8882
ISSN: 1083-4419
DOI: 10.1109/3477.979963
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