{"title":"具有时滞的中性BAMNN鲁棒性分析","authors":"Chunmei Wu, Linli Jiang","doi":"10.1109/IICSPI.2018.8690328","DOIUrl":null,"url":null,"abstract":"This paper is studied with robustness of neutral BAMNN (Bidirectional Associative Memory neural networks) with time delays. We discuss that how much neutral term contraction coefficient and time delay are allowed to ensure the global exponential stable of neutral BAMNN with time delays. By using some transcendental equations, a criterion of global exponential stable is derived for neutral BAMNN with time delays. We proved in theory, if contraction coefficient of neutral terms and time delays are smaller than the results arrived, then the BAMNN also is globally exponentially stable. Finally, an example is provided to show the correctness of our analysis and the effectiveness of the theoretical results.","PeriodicalId":6673,"journal":{"name":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","volume":"32 1","pages":"911-919"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robustness analysis of neutral BAMNN with time delays\",\"authors\":\"Chunmei Wu, Linli Jiang\",\"doi\":\"10.1109/IICSPI.2018.8690328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is studied with robustness of neutral BAMNN (Bidirectional Associative Memory neural networks) with time delays. We discuss that how much neutral term contraction coefficient and time delay are allowed to ensure the global exponential stable of neutral BAMNN with time delays. By using some transcendental equations, a criterion of global exponential stable is derived for neutral BAMNN with time delays. We proved in theory, if contraction coefficient of neutral terms and time delays are smaller than the results arrived, then the BAMNN also is globally exponentially stable. Finally, an example is provided to show the correctness of our analysis and the effectiveness of the theoretical results.\",\"PeriodicalId\":6673,\"journal\":{\"name\":\"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)\",\"volume\":\"32 1\",\"pages\":\"911-919\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IICSPI.2018.8690328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICSPI.2018.8690328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robustness analysis of neutral BAMNN with time delays
This paper is studied with robustness of neutral BAMNN (Bidirectional Associative Memory neural networks) with time delays. We discuss that how much neutral term contraction coefficient and time delay are allowed to ensure the global exponential stable of neutral BAMNN with time delays. By using some transcendental equations, a criterion of global exponential stable is derived for neutral BAMNN with time delays. We proved in theory, if contraction coefficient of neutral terms and time delays are smaller than the results arrived, then the BAMNN also is globally exponentially stable. Finally, an example is provided to show the correctness of our analysis and the effectiveness of the theoretical results.
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