Delayed Lagrangian neural networks for solving convex programming problems

被引：17
|
作者：
Li, Fen [1 ,2 ]
机构：
[1] Yunnan Vocat Coll Mech & Elect Technol, Dept Elect Engn, Kunming 650203, Peoples R China
[2] Beijing Univ Posts & Telecommun, Econ & Management Sch, Beijing 100876, Peoples R China
来源：
NEUROCOMPUTING | 2010年 / 73卷 / 10-12期
关键词：
Lagrangian programming neural networks; Delay; Global convergence; Optimization problem; Lyapunov functional; GLOBAL ASYMPTOTIC STABILITY; DUAL ASSIGNMENT NETWORKS; EXPONENTIAL STABILITY; OPTIMIZATION; CONSTRAINTS;
D O I：
10.1016/j.neucom.2010.01.009
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the global convergence is further studied for the Lagrangian programming neural networks. A delayed Lagrangian programming neural network is proposed for solving a class of convex programming problems with equality constraints. Based on Lyapunov method, it is proved that the delayed Lagrangian networks are stable and globally convergent under some conditions. Simulation examples are provided to show that the Lagrangian networks with delay are more effective than that without delays by choosing proper delays. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码：2266 / 2273
页数：8
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