Optimization of fuzzy controllers by exhaustiv, trial & error and genetic methods. Medical applications
The membership functions and rules optimization of a fuzzy controller are focused in this paper. Four optimization methods are addressed: (i) exhaustive method, (ii) trial and error, (iii) genetic algorithm, (iv) a combination of (ii) and (iii). The methods were tested in an application for anesthes...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vladimir Andrunachievici Institute of Mathematics and Computer Science
1996-07-01
|
| Series: | Computer Science Journal of Moldova |
| Subjects: | |
| Online Access: | http://www.math.md/nrofdownloads.php?file=/files/csjm/v4-n1/v4-n1-(pp69-87).pdf |
| Summary: | The membership functions and rules optimization of a fuzzy controller are focused in this paper. Four optimization methods are addressed: (i) exhaustive method, (ii) trial and error, (iii) genetic algorithm, (iv) a combination of (ii) and (iii). The methods were tested in an application for anesthesia control, using a fuzzy controller with two inputs and one output, implemented on a PC. The results obtained by the classic method, by the trial-and-error (T.E.), by genetic algorithms (G.A.) and by a mixture of G.A. & T.E. are contrasted. The number and type of the membership functions (m.f.), are also taken in account. It is shown that a combination of G.A. & T.E. gives best results in rules deduction. |
|---|---|
| ISSN: | 1561-4042 |