{"title":"基于记忆电阻的混合延迟递归神经网络的耗散结果","authors":"Kai Zhong, Song Zhu, Qiqi Yang","doi":"10.1109/ICICIP.2015.7388205","DOIUrl":null,"url":null,"abstract":"This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Dissipativity results for memristor-based recurrent neural networks with mixed delays\",\"authors\":\"Kai Zhong, Song Zhu, Qiqi Yang\",\"doi\":\"10.1109/ICICIP.2015.7388205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.\",\"PeriodicalId\":265426,\"journal\":{\"name\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2015.7388205\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2015.7388205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dissipativity results for memristor-based recurrent neural networks with mixed delays
This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.
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