Conference paper
Fuzzy rules extraction using self-organising neural network and association rules
IEEE
IEEE Region 10 International Conference on Electrical and Electronic Technology (Singapore, 19/08/2001–22/08/2001)
2001
Abstract
Fuzzy logic is becoming popular in dealing with data analysis problems that are normally handled by statistical approaches or ANNs. The major limitation is the difficulty in building the fuzzy rules from a given set of input-output data. This paper proposed a technique to extract fuzzy rules directly from input-output pairs. It uses a self-organising neural network and association rules to construct the fuzzy rule base. The self-organising neural network is first used to classify the output data by realising the probability distribution of the output space. Association rules are then used to find the relationships between the input space and the output classification, which are subsequently converted to fuzzy rules. This technique is fast and efficient. The results of an illustrative example show that the fuzzy rules extracted are promising and useful for domain experts.
Details
- Title
- Fuzzy rules extraction using self-organising neural network and association rules
- Authors/Creators
- K.W. Wong (Author/Creator) - Murdoch UniversityT. Gedeon (Author/Creator)C.C. Fung (Author/Creator)P.M. Wong (Author/Creator)
- Conference
- IEEE Region 10 International Conference on Electrical and Electronic Technology (Singapore, 19/08/2001–22/08/2001)
- Publisher
- IEEE
- Identifiers
- 991005542835907891
- Copyright
- © 2001 IEEE
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
- Note
- Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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