퍼지 결합 다항식 뉴럴 네트워크

다국어 초록 (Multilingual Abstract)

In this paper, we introduce a new fuzzy model called fuzzy combined polynomial neural networks, which are based on the representative fuzzy model named polynomial fuzzy model. In the design procedure of the proposed fuzzy model, the coefficients on consequent parts are estimated by using not general least square estimation algorithm that is a sort of global learning algorithm but weighted least square estimation algorithm, a sort of local learning algorithm. We are able to adopt various type of structures as the consequent part of fuzzy model when using a local learning algorithm. Among various structures, we select Polynomial Neural Networks which have nonlinear characteristic and the final result of which is a complex mathematical polynomial. The approximation ability of the proposed model can be improved using Polynomial Neural Networks as the consequent part.

목차 (Table of Contents)

• Abstract
• 1. 장 서론
• 2. 장 퍼지 규칙의 언어적 해석과 지역적 학습
• 3. 장 퍼지 결합 다항식 뉴럴 네트워크
• 4. 장 실험 연구 및 결과 고찰
• 5. 장 결론
• 감사의 글
• 참고문헌
• 저자소개

참고문헌 (Reference)

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