{"title":"基于控制v -稳定性的延迟递归神经网络复杂动态网络轨迹跟踪","authors":"J. Perez, Jorge A. Gonzalez, R. Soto, Joel Perez","doi":"10.1109/CERMA.2010.9","DOIUrl":null,"url":null,"abstract":"In this paper the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a dynamical network with each node being a Chen’s dynamical system.","PeriodicalId":119218,"journal":{"name":"2010 IEEE Electronics, Robotics and Automotive Mechanics Conference","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trajectory Tracking of Complex Dynamical Network for Delayed Recurrent Neural Network via Control V-Stability\",\"authors\":\"J. Perez, Jorge A. Gonzalez, R. Soto, Joel Perez\",\"doi\":\"10.1109/CERMA.2010.9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a dynamical network with each node being a Chen’s dynamical system.\",\"PeriodicalId\":119218,\"journal\":{\"name\":\"2010 IEEE Electronics, Robotics and Automotive Mechanics Conference\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Electronics, Robotics and Automotive Mechanics Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CERMA.2010.9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Electronics, Robotics and Automotive Mechanics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CERMA.2010.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trajectory Tracking of Complex Dynamical Network for Delayed Recurrent Neural Network via Control V-Stability
In this paper the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a dynamical network with each node being a Chen’s dynamical system.
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