2 May 2006 Population structure of random signal-based learning for a fuzzy logic controller design
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Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 60422D (2006) https://doi.org/10.1117/12.664655
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China
This paper proposes a population structure of random signal-based learning (PRSL), merged with simulated annealing (SA), to optimize the fuzzy logic controller (FLC). Random signal-based learning (RSL) exploits (local search) the search space very well, but it can not explore (global search) the search space because of its serial nature. To overcome these difficulties, PRSL, which consists of serial RSL as a population, was considered. Moreover, SA was added to RSL to help the exploration. The validity of the proposed algorithm was conformed by applying it to the optimization of a FLC for the inverted pendulum.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chang-Wook Han, Chang-Wook Han, Seung-Hyun Jeong, Seung-Hyun Jeong, Jung-Il Park, Jung-Il Park, } "Population structure of random signal-based learning for a fuzzy logic controller design", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60422D (2 May 2006); doi: 10.1117/12.664655; https://doi.org/10.1117/12.664655


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