A Discrete-Time Projection Neural Network for Solving Degenerate Convex Quadratic Optimization

被引：4

作者：

Zhang, Zican ^{[1
]}

Li, Chuandong ^{[1
]}

He, Xing ^{[1
]}

Huang, Tingwen ^{[2
]}

机构：

[1] Southwest Univ, Sch Elect & Informat Engn, Chongqing 400715, Peoples R China

[2] Texas A&M Univ Qatar, Dept Math, POB 23874, Doha, Qatar

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2017年 / 36卷 / 01期

关键词：

Degenerate quadratic optimization; Discrete-time neural network; Convex optimization; Algorithm convergence; GLOBAL EXPONENTIAL STABILITY;

D O I：

10.1007/s00034-016-0308-5

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a discrete-time neural network to solve convex degenerate quadratic optimization problems. Under certain conditions, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution. Compared with the existing continuous-time neural networks for degenerate quadratic optimization, the proposed neural network in this paper is more suitable for hardware implementation. Results of two experiments of this neural network are given to illustrate the effectiveness of the proposed neural network.

引用

页码：389 / 403

页数：15

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