Neuro-fuzzy based constraint programming
نویسندگان
چکیده
Constraint programmingmodels appear inmany sciences includingmathematics, engineering and physics. These problems aim at optimizing a cost function joint with some constraints. Fuzzy constraint programming has been developed for treating uncertainty in the setting of optimization problems with vague constraints. In this paper, a new method is presented into creation fuzzy concept for set of constraints. Unlike to existing methods, instead of constraints with fuzzy inequalities or fuzzy coefficients or fuzzy numbers, vague nature of constraints set ismodeled using learning schemewith adaptive neural-fuzzy inference system (ANFIS). In the proposed approach, constraints are not limited to differentiability, continuity, linearity; also the importance degree of each constraint can be easily applied. Unsatisfaction of each weighted constraint reduces membership of certainty for set of constraints.Monte-Carlo simulations areused for generating feature vector samples andoutputs for construction of necessary data for ANFIS. The experimental results show the ability of the proposed approach formodeling constrains and solving parametric programming problems. 2010 Elsevier Inc. All rights reserved.
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