Stability and L2 performance analysis of stochastic delayed neural networks.

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI:10.1109/TNN.2011.2163319
Yun Chen, Wei Xing Zheng
{"title":"Stability and L2 performance analysis of stochastic delayed neural networks.","authors":"Yun Chen,&nbsp;Wei Xing Zheng","doi":"10.1109/TNN.2011.2163319","DOIUrl":null,"url":null,"abstract":"<p><p>This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.</p>","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2163319","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on neural networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TNN.2011.2163319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2011/8/12 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28

Abstract

This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.

随机延迟神经网络的稳定性和L2性能分析。
本文主要研究一类受加性和乘性随机噪声干扰的不确定时滞神经网络的鲁棒均方指数稳定性和L(2)性能分析。基于延迟划分Lyapunov-Krasovskii泛函方法和适用于随机系统的广义Finsler引理,提出了新的均方指数稳定性和L(2)性能准则。分析结果的建立不涉及任何模型转换,交叉项的估计,额外的自由加权矩阵,或调整参数。数值算例表明,该方法具有较低的保守性和较低的计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
自引率
0.00%
发文量
2
审稿时长
8.7 months
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信