{"title":"用于系统辨识的状态空间神经网络:稳定性和复杂性","authors":"P. Gil, J. Henriques, A. Dourado, H. Duarte-Ramos","doi":"10.1109/ICCIS.2006.252333","DOIUrl":null,"url":null,"abstract":"The problem of order estimation and global stability in affine three-layered state-space neural networks is here addressed. An upper bound for the number of neurons to be inserted in the hidden layer is computed using a subspace technique. Some sufficient conditions for the global asymptotic stability are presented using the Lyapunov stability theory and the contraction mapping theorem","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"On State-Space Neural Networks for Systems Identification: Stability and Complexity\",\"authors\":\"P. Gil, J. Henriques, A. Dourado, H. Duarte-Ramos\",\"doi\":\"10.1109/ICCIS.2006.252333\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of order estimation and global stability in affine three-layered state-space neural networks is here addressed. An upper bound for the number of neurons to be inserted in the hidden layer is computed using a subspace technique. Some sufficient conditions for the global asymptotic stability are presented using the Lyapunov stability theory and the contraction mapping theorem\",\"PeriodicalId\":296028,\"journal\":{\"name\":\"2006 IEEE Conference on Cybernetics and Intelligent Systems\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Conference on Cybernetics and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIS.2006.252333\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Cybernetics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2006.252333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On State-Space Neural Networks for Systems Identification: Stability and Complexity
The problem of order estimation and global stability in affine three-layered state-space neural networks is here addressed. An upper bound for the number of neurons to be inserted in the hidden layer is computed using a subspace technique. Some sufficient conditions for the global asymptotic stability are presented using the Lyapunov stability theory and the contraction mapping theorem
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