Presentation 1998/1/23
Realization of Multi-Stage Fuzzy Infernce for Reducing the Total Number of Rules and its Application
Kangrong Tan, Shozo Tokinaga,
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Abstract(in English) This report deals with realization of multi-stage fuzzy inference system for reducing the total number of rules. In the fuzzy inference system the number of the rules grows rapidly in proportion to the power of the membership function respect to the input variables. We utilize the multi-stage fuzzy inference to reduce the total number of rules where the input variables are partly used in each stage of inference. At first, the optimization of the parameters of the fuzzy system in given based upon the back-propagation algorithm as in the neural networks. The order of the usage of input variables, and the shape of the membership function is discussed. As an application, the classification of the corporate bonds by using the multi-stage fuzzy inference is copmared with the single-stage inference and conventional mehtods.
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Keyword(in English) multi-stage fuzzy inference / fuzzy rules / back-propagation / learning / bond rating
Paper # SAT97-118
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Committee SAT
Conference Date 1998/1/23(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Realization of Multi-Stage Fuzzy Infernce for Reducing the Total Number of Rules and its Application
Sub Title (in English)
Keyword(1) multi-stage fuzzy inference
Keyword(2) fuzzy rules
Keyword(3) back-propagation
Keyword(4) learning
Keyword(5) bond rating
1st Author's Name Kangrong Tan
1st Author's Affiliation Department of Economic Engineering, Faculty of Economics Kyushu University()
2nd Author's Name Shozo Tokinaga
2nd Author's Affiliation Department of Economic Engineering, Faculty of Economics Kyushu University
Date 1998/1/23
Paper # SAT97-118
Volume (vol) vol.97
Number (no) 487
Page pp.pp.-
#Pages 8
Date of Issue