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Temperature Control by On-line CFCM-based Adaptive Neuro-Fuzzy System  

윤기후 (주성대학 디지털정보제어학과)
곽근창 (충북대학교 전기공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea TE / v.39, no.4, 2002 , pp. 414-422 More about this Journal
Abstract
In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.
Keywords
temperature control; CFCM clustering; adaptive neuro-fuzzy system;
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