NDLI: An accurate COG defuzzifier design by the co-adaptation of learning and evolution

Content Provider	IEEE Xplore Digital Library
Author	Daijin Kim
Copyright Year	2000
Description	Author affiliation: Dept. of Comput. Sci. & Eng., POSTECH, Pohang, South Korea (Daijin Kim)
Abstract	This paper proposes an accurate center of gravity (COG) defuzzification method that improves both the system's approximation ability and the control performance of a fuzzy logic controller. The accuracy of the proposed COG defuzzifier is obtained by representing the output membership functions (MFs) with various design parameters such as the centers, widths, and modifiers of MFs and by adjusting these design parameters with Lamarckian co-adaptation of learning and evolution, where the learning performs a local search of design parameters in an individual COG defuzzifier, but the evolution performs a global search of design parameters among a population of various COG defuzzifiers. This co-adaptation scheme allows it to evolve much faster than the nonlearning case and gives a higher possibility of finding an optimal solution due to its wider searching capability. An application to the truck backer-upper control problem of the proposed co-adaptive design method of COG defuzzifier is presented.
Starting Page	741
Ending Page	747
File Size	638365
Page Count	7
File Format	PDF
ISBN	0780358775
ISSN	10987584
DOI	10.1109/FUZZY.2000.839124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gravity Control systems Fuzzy logic Mathematical model Message-oriented middleware Design methodology Error correction Consumer products Industrial control Process control
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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