{"title":"分数阶离散神经网络的鲁棒控制","authors":"Mellah Mohamed, Ouannas Adel","doi":"10.1109/ICRAMI52622.2021.9585959","DOIUrl":null,"url":null,"abstract":"This paper aims to present a general approach to control fractional discrete neural networks. We prove a new theorem, which ensures the stabilization of some fractional discrete neural networks class’s utilzing the Lyapunov approach. A numerical example and simulation results are reported to confirm the stabilization approach efficiency.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Robust Control of Fractional Discrete Neural Networks\",\"authors\":\"Mellah Mohamed, Ouannas Adel\",\"doi\":\"10.1109/ICRAMI52622.2021.9585959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to present a general approach to control fractional discrete neural networks. We prove a new theorem, which ensures the stabilization of some fractional discrete neural networks class’s utilzing the Lyapunov approach. A numerical example and simulation results are reported to confirm the stabilization approach efficiency.\",\"PeriodicalId\":440750,\"journal\":{\"name\":\"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRAMI52622.2021.9585959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRAMI52622.2021.9585959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Robust Control of Fractional Discrete Neural Networks
This paper aims to present a general approach to control fractional discrete neural networks. We prove a new theorem, which ensures the stabilization of some fractional discrete neural networks class’s utilzing the Lyapunov approach. A numerical example and simulation results are reported to confirm the stabilization approach efficiency.
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