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Aspects of Genetic Algorithm-Designed Fuzzy Logic Controllers.

PB95136305

Publication Date	1994
Personal Author	Karr, C. L.; Fleming, J. W.; Vann, P. A.
Page Count	22
Abstract	The research described in the report is twofold. First, the basic approach to developing a fuzzy logic controller (FLC) using genetic algorithms (GA's) is presented. The GA-designed FLC is developed for a specific physical system, a pH titration system. Second, empirical results are presented in which variations in the FLC implementation are compared. Specifically, the effects of altering five aspects of the pH FLC are considered: (1) membership function form, (2) number of fuzzy classes, (3) the center-of-area method, (4) implication operator, and (5) fuzzy rule form. Results indicate that the technique in which GA's are used to design FLC's is effective, and when this technique is used, variations in FLC implementation have little effect on FLC performance in a chosen control problem.
Keywords	Process control Fuzzy logic Genetic algorithms Computer aided control systems Controllers Artificial intelligence Control systems design pH Titration Graphs(Charts)
Source Agency	Bureau of Mines
NTIS Subject Category	94A - Production Planning & Process Controls 62 - Computers, Control & Information Theory
Corporate Authors	Bureau of Mines, University, AL. Tuscaloosa Research Center.
Document Type	Technical Report
Title Note	Report of investigations/1994.
NTIS Issue Number	199504

Aspects of Genetic Algorithm-Designed Fuzzy Logic Controllers.

Aspects of Genetic Algorithm-Designed Fuzzy Logic Controllers.
PB95136305

Publication Date	1994
Personal Author	Karr, C. L.; Fleming, J. W.; Vann, P. A.
Page Count	22
Abstract	The research described in the report is twofold. First, the basic approach to developing a fuzzy logic controller (FLC) using genetic algorithms (GA's) is presented. The GA-designed FLC is developed for a specific physical system, a pH titration system. Second, empirical results are presented in which variations in the FLC implementation are compared. Specifically, the effects of altering five aspects of the pH FLC are considered: (1) membership function form, (2) number of fuzzy classes, (3) the center-of-area method, (4) implication operator, and (5) fuzzy rule form. Results indicate that the technique in which GA's are used to design FLC's is effective, and when this technique is used, variations in FLC implementation have little effect on FLC performance in a chosen control problem.
Keywords	Process control Fuzzy logic Genetic algorithms Computer aided control systems Controllers Artificial intelligence Control systems design pH Titration Graphs(Charts)
Source Agency	Bureau of Mines
NTIS Subject Category	94A - Production Planning & Process Controls 62 - Computers, Control & Information Theory
Corporate Authors	Bureau of Mines, University, AL. Tuscaloosa Research Center.
Document Type	Technical Report
Title Note	Report of investigations/1994.
NTIS Issue Number	199504