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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
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- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.14 No.4
- 발행연도
- 2014.12
- 수록면
- 276 - 287 (12page)
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초록· 키워드
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Neural fuzzy system (NFS) is basically a fuzzy system that has been equipped with learning capability adapted from the learning idea used in neural networks. Due to their outstanding system modeling capability, NFS have been widely employed in various applications. In this article, we intend to discuss several ideas regarding the learning of NFS for modeling systems. The first issue discussed here is about structure learning techniques. Various ideas used in the literature are introduced and discussed. The second issue is about the use of recurrent networks in NFS to model dynamic systems. The discussion about the performance of such systems will be given. It can be found that such a delay feedback can only bring one order to the system not all possible order as claimed in the literature. Finally, the mechanisms and relative learning performance of with the use of the recursive least squares (RLS) algorithm are reported and discussed. The analyses will be on the effects of interactions among rules. Two kinds of systems are considered. They are the strict rules and generalized rules and have difference variances for membership functions. With those observations in our study, several suggestions regarding the use of the RLS algorithm in NFS are presented.
목차
Abstract
1. Introduction
2. General Description of NFS
3. Structure Learning for NFS
4. Delay Feedback and Dynamic Modeling
5. Analysis of the Use of RLS
6. Conclusions
References
참고문헌 (58)
참고문헌 신청
1. [단행본] - B. Kosko / 1992 / Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence / Prentice Hall
2. [단행본] - C. T. Lin / 1996 / Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems / Prentice Hall PTR
3. [학술지(정기간행물)] - Kurt Hornik / 1989 / Multilayer feedforward networks are universal approximators / Neural Networks 2 (5) : 359 ~ 366
4. [학술지(정기간행물)] - Lotfi A. Zadeh / 1973 / Outline of a New Approach to the Analysis of Complex Systems and Decision Processes / IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (1) : 28 ~ 44
5. [학술지(정기간행물)] - WITOLD PEDRYCZ / 2007 / STRUCTURED FUZZY MODELS / Cybernetics and Systems 16 (1) : 103 ~ 117
2. [단행본] - C. T. Lin / 1996 / Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems / Prentice Hall PTR
3. [학술지(정기간행물)] - Kurt Hornik / 1989 / Multilayer feedforward networks are universal approximators / Neural Networks 2 (5) : 359 ~ 366
4. [학술지(정기간행물)] - Lotfi A. Zadeh / 1973 / Outline of a New Approach to the Analysis of Complex Systems and Decision Processes / IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (1) : 28 ~ 44
5. [학술지(정기간행물)] - WITOLD PEDRYCZ / 2007 / STRUCTURED FUZZY MODELS / Cybernetics and Systems 16 (1) : 103 ~ 117
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