Designing fuzzy logic controllers by genetic algorithms considering their characteristics

2002 ◽  
Author(s):  
Seihwan Park ◽  
H. Lee-Kwang
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controllers ◽  
Logic Controllers

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

2015 ◽  
Vol 14 (05) ◽  
pp. 1063-1092 ◽  
Author(s):  
Pintu Chandra Shill ◽  
M. A. H. Akhand ◽  
MD. Asaduzzaman ◽  
Kazuyuki Murase
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Degrees Of Freedom ◽  
Rule Learning ◽  
Size Reduction ◽  
Rule Base ◽  
Second Phase ◽  
Fuzzy Logic Controllers ◽  
Base Size ◽  
Logic Controllers

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.

Genetic algorithms with variable mutation rates: Application to fuzzy logic controller design

1997 ◽  
Vol 211 (2) ◽  
pp. 157-167 ◽  
Author(s):  
D T Pham ◽  
D Karaboga
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Mutation Rate ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Mutation Rates ◽  
Fuzzy Logic Controllers ◽  
Delayed System ◽  
Simulation Results ◽  
Logic Controllers

Three variable mutation rate strategies for improving the performance of genetic algorithms (GAs) are described. The problem of optimizing fuzzy logic controllers is used to evaluate a GA adopting these strategies against a GA employing a static mutation regime. Simulation results for a second-order time-delayed system controlled by fuzzy logic controllers (FLCs) obtained using the different GAs are presented.

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