S. Espejo, Á. Rodríguez-Vázquez, R. Domínguez-Castro, R. Carmona
{"title":"FSR CNN模型的收敛性和稳定性","authors":"S. Espejo, Á. Rodríguez-Vázquez, R. Domínguez-Castro, R. Carmona","doi":"10.1109/CNNA.1994.381640","DOIUrl":null,"url":null,"abstract":"Stability and convergency results are reported for a modified continuous-time CNN model. The signal range of the state variables is equal to the unitary interval, independently of the application, Stability and convergency properties are similar to those of the original model and, for given templates and offset coefficients. The results are generally identical. In addition, robustness and area-efficiency of VLSI implementations are significantly advantageous.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Convergence and stability of the FSR CNN model\",\"authors\":\"S. Espejo, Á. Rodríguez-Vázquez, R. Domínguez-Castro, R. Carmona\",\"doi\":\"10.1109/CNNA.1994.381640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stability and convergency results are reported for a modified continuous-time CNN model. The signal range of the state variables is equal to the unitary interval, independently of the application, Stability and convergency properties are similar to those of the original model and, for given templates and offset coefficients. The results are generally identical. In addition, robustness and area-efficiency of VLSI implementations are significantly advantageous.<<ETX>>\",\"PeriodicalId\":248898,\"journal\":{\"name\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1994.381640\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1994.381640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stability and convergency results are reported for a modified continuous-time CNN model. The signal range of the state variables is equal to the unitary interval, independently of the application, Stability and convergency properties are similar to those of the original model and, for given templates and offset coefficients. The results are generally identical. In addition, robustness and area-efficiency of VLSI implementations are significantly advantageous.<>
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