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Neuro-fuzzy based constraint programming

机译：基于神经模糊的约束规划

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摘要

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.
机译：约束编程模型出现在许多科学中，包括数学，工程学和物理学。这些问题旨在优化具有某些约束的成本函数联合。已经开发了模糊约束规划，用于处理含模糊约束的优化问题中的不确定性。本文提出了一种新的约束集创建模糊概念的方法。与现有方法不同，使用具有自适应神经模糊推理系统（ANFIS）的学习方案对约束集的模糊性质进行建模，而不是使用模糊不等式，模糊系数或模糊数进行约束。在所提出的方法中，约束条件不限于可微性，连续性，线性性。每个约束的重要程度也可以很容易地应用。每个加权约束的不满足会降低一组约束的确定性。蒙特卡洛模拟用于生成特征向量样本和输出，以构建ANFIS的必要数据。实验结果表明，该方法具有建模约束和解决参数化编程问题的能力。

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