Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms
Pintu Chandra Shill (),
M. A. H. Akhand (),
Md. Asaduzzaman () and
Kazuyuki Murase ()
Additional contact information
Pintu Chandra Shill: Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
M. A. H. Akhand: Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
Md. Asaduzzaman: Department of System Design Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-910-8507, Japan
Kazuyuki Murase: Department of System Design Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-910-8507, Japan
International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 05, 1063-1092
Abstract:
In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the first phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the conflicting rules during the learning process. The first and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods.
Keywords: Fuzzy logic controllers; rule base size reduction; binary and real coded couple genetic algorithms; optimization; two degrees freedom inverted pendulum (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622015500273
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:14:y:2015:i:05:n:s0219622015500273
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219622015500273
Access Statistics for this article
International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi
More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().