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http://dx.doi.org/10.6109/JKIICE.2009.13.3.505

A study of MIMO Fuzzy system with a Learning Ability  

Park, Jin-Hyun (진주산업대학교 메카트로닉스공학과)
Bae, Kang-Yul (진주산업대학교 메카트로닉스공학과)
Choi, Young-Kiu (부산대학교 전자전기정보컴퓨터공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.13, no.3, 2009 , pp. 505-513 More about this Journal
Abstract
Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. But the most of fuzzy systems are difficult to make fuzzy inference rules in the case of MIMO system. The past days, We had proposed the MIMO fuzzy inference which had extended a Z. Cao's fuzzy inference to handle MIMO system. But many times and effort needed to determine the relation matrix elements of MIMO fuzzy inference by heuristic and trial and error method in order to improve inference performances. In this paper, we propose a MIMO fuzzy inference method with the learning ability witch is used a gradient descent method in order to improve the performances. Through the computer simulation studies for the inverse kinematics problem of 2-axis robot, we show that proposed inference method using a gradient descent method has good performances.
Keywords
MIMO fuzzy inference; Z. Cao's fuzzy inference; mamdani's fuzzy inference; gradient descent learning;
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