{"title":"时变时滞复值神经网络的拉格朗日稳定性","authors":"Zhengwen Tu, Jinde Cao","doi":"10.1109/ICACI.2016.7449850","DOIUrl":null,"url":null,"abstract":"In this paper, the Lagrange stability of complex-valued neural networks(CVNNs) with time-varying delays is considered. By employing matrix measure approach and generalized Halanay inequality, several sufficient criteria are derived to ascertain the global Lagrange stability for the addressed neural networks. Meanwhile, the globally exponentially attractive sets are exhibited. Finally, two numerical examples are presented to verify our theoretical results.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lagrange stability of complex-valued neural networks with time-varying delays\",\"authors\":\"Zhengwen Tu, Jinde Cao\",\"doi\":\"10.1109/ICACI.2016.7449850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the Lagrange stability of complex-valued neural networks(CVNNs) with time-varying delays is considered. By employing matrix measure approach and generalized Halanay inequality, several sufficient criteria are derived to ascertain the global Lagrange stability for the addressed neural networks. Meanwhile, the globally exponentially attractive sets are exhibited. Finally, two numerical examples are presented to verify our theoretical results.\",\"PeriodicalId\":211040,\"journal\":{\"name\":\"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACI.2016.7449850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI.2016.7449850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lagrange stability of complex-valued neural networks with time-varying delays
In this paper, the Lagrange stability of complex-valued neural networks(CVNNs) with time-varying delays is considered. By employing matrix measure approach and generalized Halanay inequality, several sufficient criteria are derived to ascertain the global Lagrange stability for the addressed neural networks. Meanwhile, the globally exponentially attractive sets are exhibited. Finally, two numerical examples are presented to verify our theoretical results.