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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2017.11
- 수록면
- 123 - 126 (4page)
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초록· 키워드
오류제보하기
In this study, we introduce and discuss a concept of an incremental RBFN model. In contrast to typical rule-based systems encountered in fuzzy modeling, the underlying principle exploited here is to consider a two-phase development of fuzzy models. First, we build a standard regression model which could be treated as a preliminary construct capturing the quadratic polynomials part of the data and in this way forming a backbone of the entire construct. Next, all modeling discrepancies are compensated by a collection of rules that become attached to the regions of the input space in which the error becomes localized. The incremental model is constructed by building a collection of information granules through some specialized fuzzy clustering, called context-based (conditional) Fuzzy C-Means that is guided by the distribution of error of the quadratic polynomials part of the model. The architecture of the model is discussed along with the major algorithmic phases of its development. In particular, the issue of granularity of fuzzy sets of context and induced clusters is discussed vis-à-vis the performance of the model.
목차
Abstract
1. 서론
2. 방사기저 함수 신경망
3. 점증적인 방사기저 함수 신경망
4. 실험 및 결과
4. 결론
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