On the recurrent neural networks for solving general quadratic programming problems

被引：0
|
作者：
Mladenov, V [1 ]
机构：
[1] Tech Univ Sofia, Dept Theory Elect Engn, Fac Automat, Sofia 1000, Bulgaria
来源：
NEUREL 2004: SEVENTH SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2004年
关键词：
quadratic programming problems; Kuhn-Tucker conditions; neural networks;
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：
Quadratic programming problems are widespread, class of nonlinear programming problems with many practical applications. The case of inequality constraints have been considered in a previous author's paper. In this contribution an extension of these results for the case of inequality and equality constraints is presented. Based on equivalent formulation of Kuhn-Tucker conditions, a new neural network for solving the general quadratic programming problems, for the case of both inequality and equality constraints, is proposed. Two theorems for global stability and convergence of this network are given as well. The presented network has lower complexity for implementations and the. examples confirm its effectiveness. Simulation results based on SIMULINK (R) models are given and compared.
引用
收藏
页码：5 / 9
页数：5
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