지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 제어로봇시스템학회 International Journal of Control, Automation, and Systems International Journal of Control, Automation, and Systems 제6권 제5호
- 발행연도
- 2008.10
- 수록면
- 755 - 766 (12page)
이용수
초록· 키워드
오류제보하기
This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.
목차
Abstract
1. INTRODUCTION
2. COMPENSATORY OPERATION
3. STRUCTURE OF THE RECURRENT COMPENSATORY FUZZY NEURAL NETWORK
4. LEARNING ALGORITHMS OF RCFNN
5. SIMULATION RESULTS
6. CONCLUSIONS
REFERENCES
참고문헌 (18)
참고문헌 신청
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C. F. Juang , 1998 , An on-line self-constructing neural fuzzy inference network and its applications , IEEE Trans. on Fuzzy Systems 6 (1) : 12 ~ 31
F. J. Lin , 2001 , Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive , IEEE Trans. on Fuzzy Systems 9 : 751 ~ 759
J. S. R. Jang , 1993 , ANFIS: Adaptive-network-based fuzzy inference system , IEEE Trans. on Systems Man and Cybernetics 23 (3) : 665 ~ 685
C. J. Lin , 1996 , Reinforcement learning for an ART-based fuzzy adaptive learning control network , IEEE Trans. Neural Networks 7 : 709 ~ 731
C. F. Juang , 1998 , An on-line self-constructing neural fuzzy inference network and its applications , IEEE Trans. on Fuzzy Systems 6 (1) : 12 ~ 31
F. J. Lin , 2001 , Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive , IEEE Trans. on Fuzzy Systems 9 : 751 ~ 759
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UCI(KEPA) : I410-ECN-0101-2013-569-003244332