Hybrid Hopfield Architecture for Solving Nonlinear Programming Problems

被引：0

作者：

Bertoni, Fabiana Cristina ^{[1
,2
]}

da Silva, Ivan Nunes ^{[1
,2
]}

机构：

[1] State Univ Feira de Santana, Dept Comp Engn, BR-44031460 Feira De Santana, BA, Brazil

[2] Univ Sao Paulo, Dept Elect Engn, BR-13566590 Sao Carlos, SP, Brazil

来源：

NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS | 2009年 / 5863卷

关键词：

Hopfield network; genetic algorithms; nonlinear programming;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a neurogenetic approach for solving nonlinear programming problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent; feasible solutions for nonlinear programming problems.

引用

页码：267 / +

页数：3

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