{"title":"具有定向拓扑和时变延迟的多权重耦合神经网络的有限时间被动性","authors":"","doi":"10.1016/j.neucom.2024.128581","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, the finite-time passivity (FTP) problem for multi-weighted coupled neural networks (MWCNNs) with directed topologies and time-varying delay is discussed. Firstly, by designing a new state feedback controller, several FTP criteria are given for the considered network. Then, some finite-time synchronization (FTS) criteria are established by employing the FTP results. Secondly, a hybrid impulsive and state feedback controller is first designed, under which different FTP and FTS criteria are presented and the synchronization time is successfully shortened compared to the non-hybrid controller without impulses. Finally, numerical simulations are given to show the effectiveness and superiority of the obtained results.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Finite-time passivity of multi-weighted coupled neural networks with directed topologies and time-varying delay\",\"authors\":\"\",\"doi\":\"10.1016/j.neucom.2024.128581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, the finite-time passivity (FTP) problem for multi-weighted coupled neural networks (MWCNNs) with directed topologies and time-varying delay is discussed. Firstly, by designing a new state feedback controller, several FTP criteria are given for the considered network. Then, some finite-time synchronization (FTS) criteria are established by employing the FTP results. Secondly, a hybrid impulsive and state feedback controller is first designed, under which different FTP and FTS criteria are presented and the synchronization time is successfully shortened compared to the non-hybrid controller without impulses. Finally, numerical simulations are given to show the effectiveness and superiority of the obtained results.</p></div>\",\"PeriodicalId\":19268,\"journal\":{\"name\":\"Neurocomputing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurocomputing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925231224013523\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224013523","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Finite-time passivity of multi-weighted coupled neural networks with directed topologies and time-varying delay
In this paper, the finite-time passivity (FTP) problem for multi-weighted coupled neural networks (MWCNNs) with directed topologies and time-varying delay is discussed. Firstly, by designing a new state feedback controller, several FTP criteria are given for the considered network. Then, some finite-time synchronization (FTS) criteria are established by employing the FTP results. Secondly, a hybrid impulsive and state feedback controller is first designed, under which different FTP and FTS criteria are presented and the synchronization time is successfully shortened compared to the non-hybrid controller without impulses. Finally, numerical simulations are given to show the effectiveness and superiority of the obtained results.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.