ZNN for solving online time-varying linear matrix-vector inequality via equality conversion

被引：45

作者：

Guo, Dongsheng ^{[1
]}

Zhang, Yunong ^{[1
]}

机构：

[1] Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2015年 / 259卷

基金：

中国国家自然科学基金;

关键词：

Zhang neural network (ZNN); Time-varying; Linear matrix-vector inequality (LMVI); Conversion; ZNN design formula; ZHANG NEURAL-NETWORK; OBSTACLE-AVOIDANCE; DYNAMIC-SYSTEM; ALGORITHMS; VARIANT;

D O I：

10.1016/j.amc.2015.02.060

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a special recurrent neural network termed Zhang neural network (ZNN) is proposed and investigated for solving online time-varying linear matrix-vector inequality (LMVI) via equality conversion. That is, by introducing a time-varying vector (of which each element is great than or equal to zero), such a time-varying linear inequality can be converted to a time-varying matrix-vector equation. Then, the ZNN model is developed and investigated for solving online the time-varying matrix-vector equation (as well as the time-varying LMVI) by employing the ZNN design formula. The resultant ZNN model exploits the time-derivative information of time-varying coefficients. Computer-simulation results further demonstrate the efficacy and superiority of the proposed ZNN model for solving online the time-varying LMVI (and the converted time-varying matrix-vector equation). (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：327 / 338

页数：12

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