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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 International Journal of Control Automation and Systems International Journal of Control Automation and System Vol.6 No.6
- 발행연도
- 2008.12
- 수록면
- 928 - 938 (11page)
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초록· 키워드
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This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function Ψ and a penalty function τ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro-fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.
목차
Abstract
1. INTRODUCTION
2. THE HYPERPLANE CLUSTERING ALGORITHM [1]
3. BASIC DEFINITIONS
4. PURE FUNCTION, PENALTY FUNCTION AND ALGORITHM CSHL
5. ALGORITHM HLM1
6. ALGORITHM HLM2
7. NUMERICAL EXPERIMENTS
8. CONCLUSION
REFERENCES
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