Performance Improvement of the FLC by Membership Function Modification Algorithm


The KIPS Transactions:PartB , Vol. 8, No. 2, pp. 123-129, Apr. 2001
10.3745/KIPSTB.2001.8.2.123,   PDF Download:

Abstract

We, in this paper, propose the membership function modification algorithm, which can improve the performance of the fuzzy logic controller (FLC) by representing the control knowledge of the experts and the operators more exactly. Our algorithm modifies the size and shape of the fuzzy membership function based on the input-output data clustering so that the FLC can represent the control knowledge more exactly. It uses the rough control knowledge retrieved from the intuitive knowledge and experience as the evaluation criteria for clustering the input-output data. We apply our algorithm to the model for controlling the water level and for controlling the traffic signal, and show that it can improve the performance of the existing FLCs and can solve the difficulty of the range partition for the linguistic variables to a certain extent.


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Cite this article
[IEEE Style]
W. K. Choi and M. J. Jeong, "Performance Improvement of the FLC by Membership Function Modification Algorithm," The KIPS Transactions:PartB , vol. 8, no. 2, pp. 123-129, 2001. DOI: 10.3745/KIPSTB.2001.8.2.123.

[ACM Style]
Wan Kyoo Choi and Moon Jai Jeong. 2001. Performance Improvement of the FLC by Membership Function Modification Algorithm. The KIPS Transactions:PartB , 8, 2, (2001), 123-129. DOI: 10.3745/KIPSTB.2001.8.2.123.