Tuning fuzzy control rules by the α constrained method which solves constrained nonlinear optimization problems

摘要

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.