RISS 검색 - 국내학술지논문 상세보기

다국어 초록 (Multilingual Abstract)

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

목차 (Table of Contents)

Abstract
1. 서론
2. 퍼지 규칙의 후반부 구조 지역적 학습
3. 퍼지 결합 다항식 뉴럴 네트워크 기반 패턴 분류기
4. 시뮬레이션 및 결과 고찰

Abstract
1. 서론
2. 퍼지 규칙의 후반부 구조 지역적 학습
3. 퍼지 결합 다항식 뉴럴 네트워크 기반 패턴 분류기
4. 시뮬레이션 및 결과 고찰
5. 결론
References

참고문헌 (Reference)

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