{"title":"反应-扩散惯性神经网络不完全测量的指数状态估计","authors":"Xuemei Wang, Xiaona Song, Jingtao Man, Nana Wu","doi":"10.1080/23335777.2021.2014978","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this paper, the problem of exponential state estimation for inertial neural networks with reaction-diffusion term (RDINNs) via incomplete measurement scheme is investigated. Unlike the full measurement method, this method estimates the system by measuring the state of partially available neurons. First, by constructing an appropriate variable substitution, the second-order system is transformed into a first-order one. Then, a suitable Lyapunov-krasovskii function (LKF) is constructed, and sufficient conditions for the stability of the system are obtained . Finally, the practicality and effectiveness of the proposed method is further verified by two numerical examples.","PeriodicalId":37058,"journal":{"name":"Cyber-Physical Systems","volume":"49 1","pages":"357 - 375"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exponential state estimation for reaction-diffusion inertial neural networks via incomplete measurement scheme\",\"authors\":\"Xuemei Wang, Xiaona Song, Jingtao Man, Nana Wu\",\"doi\":\"10.1080/23335777.2021.2014978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT In this paper, the problem of exponential state estimation for inertial neural networks with reaction-diffusion term (RDINNs) via incomplete measurement scheme is investigated. Unlike the full measurement method, this method estimates the system by measuring the state of partially available neurons. First, by constructing an appropriate variable substitution, the second-order system is transformed into a first-order one. Then, a suitable Lyapunov-krasovskii function (LKF) is constructed, and sufficient conditions for the stability of the system are obtained . Finally, the practicality and effectiveness of the proposed method is further verified by two numerical examples.\",\"PeriodicalId\":37058,\"journal\":{\"name\":\"Cyber-Physical Systems\",\"volume\":\"49 1\",\"pages\":\"357 - 375\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cyber-Physical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23335777.2021.2014978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cyber-Physical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23335777.2021.2014978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Exponential state estimation for reaction-diffusion inertial neural networks via incomplete measurement scheme
ABSTRACT In this paper, the problem of exponential state estimation for inertial neural networks with reaction-diffusion term (RDINNs) via incomplete measurement scheme is investigated. Unlike the full measurement method, this method estimates the system by measuring the state of partially available neurons. First, by constructing an appropriate variable substitution, the second-order system is transformed into a first-order one. Then, a suitable Lyapunov-krasovskii function (LKF) is constructed, and sufficient conditions for the stability of the system are obtained . Finally, the practicality and effectiveness of the proposed method is further verified by two numerical examples.
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