基于神经网络的多目标优化模型的模糊解法

Fuzzy Multi-Objective Optimization Methods Based on Functional-Link Network

概述了多目标优化的常规解法 ,研究了多目标优化的模糊求解原理 ,提出基于函数联接神经网络的多目标优化模型的模糊解法。计算结果表明 ,函数联接神经网络不仅算法简单 ,而且具有很强的非线性插值能力 ,能较好地表达模糊集的隶属函数 ,解决了应用模糊集理论处理多目标优化问题时很难合理确定隶属函数表达式这一关键问题。

Conventional methods of solving multi-objective optimization problems are reviewed, and the principle of solving multi-objective optimization problems with fuzzy sets theory is studied. However, membership function is the key to introduce the fuzzy sets theory to multi-objective optimization, it is difficult to set up membership functions in practical engineering. On the basis of the capability of interpolation of functional-link network, discrete membership functions are used as training samples. When the network converges, the continuous membership functions implemented with the network. Membership functions based on functional-link networks have been used in multi-objective optimization.