Neuro-fuzzy based constraint programming

4Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.

Abstract

Constraint programming models appear in many sciences including mathematics, 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 is modeled using learning scheme with 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 are used for generating feature vector samples and outputs for construction of necessary data for ANFIS. The experimental results show the ability of the proposed approach for modeling constrains and solving parametric programming problems. © 2010 Elsevier Inc.

Cite

CITATION STYLE

APA

Yazdi, H. S., Hosseini, S. E., & Yazdi, M. S. (2010). Neuro-fuzzy based constraint programming. Applied Mathematical Modelling, 34(11), 3547–3559. https://doi.org/10.1016/j.apm.2010.03.005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free