An Investigation of Fuzzy Model Using AIC
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概要
- 論文の詳細を見る
In this paper we suggest the "goodness" of models using the information criterion AIC. The information criterion AIC is a statistic to estimate the badness of models. When we usually make the fuzzy rules, we aim to minimize inference error and the number of rules. But these conditions are the criteria to acquire an optimum rule-model by using the training data. In the general case of fuzzy reasoning, we aim to minimize the inference error for not only given training data, but also unknown data. So we have introduced a new information criterion based on AIC into the appraised criterion for estimating the acquired fuzzy rules. Experimental results are given to show the validity of using AIC.
- 社団法人電子情報通信学会の論文
- 1997-09-25
著者
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Fukumoto Shinya
Faculty Of Engineering Kagoshima University
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KISHIDA Kazuya
Department of Electronic Control Engineering, Kagoshima National College of Technology
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FUKUMOTO Shinya
the Faculty of Engineering, Kagoshima University
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MIYAJIMA Hiromi
the Faculty of Engineering, Kagoshima University
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KISHIDA Kazuya
the Faculty of Engineering, Kagoshima University
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NAGASAWA Yoji
the Faculty of Engineering, Kagoshima University
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MIYAJIMA Hiromi
Department of Electrical and Electronics Engineering, Faculty of Engineering, Kagoshima University
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Nagasawa Yoji
The Faculty Of Engineering Kagoshima University
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Kishida K
Department Of Electronic Control Engineering Kagoshima National College Of Technology
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Kishida Kazuya
The Faculty Of Engineering Kagoshima University
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Miyajima H
Department Of Electrical And Electronics Engineering Faculty Of Engineering Kagoshima University
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Miyajima Hiromi
The Faculty Of Engineering Kagoshima University
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