퍼지균등화와 러프집합을 이용한 선박설계 지식기반 구축
Knowledge Base Construction of Ship Design Using Fuzzy Equalization and Rough Sets
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Inference rules of the knowledge base, generated by experts or optimization, may be often inconsistent and incomplete. This paper suggests a systematic and automatic method which extracts inference rules not from experts' subject but from data. First, input/output linguistic variables are partitioned into several properties by the fuzzy equalization algorithm and each combination of their properties comes to premise of inference rule. Then, the conclusion which is the most suitable for the premise is selected by evaluating consistent measure. This method, automatically from data, derives inference rules from experience. It is shown through application that extracts new inference rules between hull dimensions and hull performance.
Abstract
Inference rules of the knowledge base, generated by experts or optimization, may be often inconsistent and incomplete. This paper suggests a systematic and automatic method which extracts inference rules not from experts' subject but from data. First, input/output linguistic variables are partitioned into several properties by the fuzzy equalization algorithm and each combination of their properties comes to premise of inference rule. Then, the conclusion which is the most suitable for the premise is selected by evaluating consistent measure. This method, automatically from data, derives inference rules from experience. It is shown through application that extracts new inference rules between hull dimensions and hull performance.
참고문헌 (5)
- "퍼지균등화와 유전알고리즘에 의한 자동적인 퍼지 규칙 생성", pp.121~125 (2001)
- "전문가 시스템 개발을 위한 체계적인 규칙추출 프로세스 방안", pp.79~88 (1999)
- "Rough Set을 이용한 퍼지 규칙의 생성", pp.327~332 (1996)
- "Theoretical Aspects of Reasoning About Data" (1992), . Rough Sets Kluwer Academic Publishers
- "Fuzzy equalization in the construction of fuzzy sets", 119, pp.329~335 (2001)