Tuning fuzzy control rules by the α constrained method which solves constrained nonlinear optimization problems
✍ Scribed by Tetsuyuki Takahama; Setsuko Sakai
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 372 KB
- Volume
- 83
- Category
- Article
- ISSN
- 1042-0967
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✦ Synopsis
Learning of fuzzy control rules can be considered as solving a constrained nonlinear optimization problem, in which the objective function is not differentiable. In this case, usually the problem is solved by the combination of a direct search method and penalty method. However, it is difficult to know what value of the penalty coefficient leads to a feasible solution and how much a search point for a solution satisfies constraints. In this research, we represent the satisfaction level of constraints by fuzzy constraints of fuzzy programming. We propose D level comparison, which compares the search points based on the satisfaction level. We propose the D constrained method, which converts constrained problems to unconstrained problems using D level comparisons. We also propose the D constrained Powell method by applying D constrained method to Powells direct search method. Through some examples and the learning of fuzzy control rules, we show that a feasible solution can be obtained easily by our method with confirming the satisfaction level.