• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.552-555
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• 1998

In dealing with representing knowledge under uncertainty there is a sustain tendency to increase flexibility in order to avoid problems of inconsistency in the knowledge. Many knowledge systems(information retrieval systems, expert system) include hybrid representation models. Funny retrieval systems appear as a complement or as an enrichment of this models. In this paper, we describe dynamic rule modification through situation assessment for uncertainty management.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.449-454
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• 2000

A new fuzzy logic control design algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is widely used for design of experiments in statistics and industrial engineering. The essence of the algorithm is to introduce Nth-certainty factor defined from the F-value of the ANOVA(analysis of variance) table, in order to effectively exclude the less confident rules. The proposed algorithm with multi-objective decision table(MODT) is found to be capable of the detection of inconsistency and the rule classification, reduction and modification. It is also shown that the algorithm can be successfully applied to the fuzzy controller design of an active magnetic bearing system.