{"title":"基于BP神经网络的昼夜节律信号源系统识别","authors":"Y. Cisse, Y. Kinouchi, H. Nagashino, M. Akutagawa","doi":"10.1109/IEMBS.1998.746130","DOIUrl":null,"url":null,"abstract":"The dynamics of some biological signal sources may be identified through measured data. The regulating characteristics of the wake-sleep circadian rhythm is identified here as an example by using BP neural networks. As a result, a MA model of neural networks can acquire the characteristics. The change of wake-sleep period is almost controlled suppressively by the data of preceding three days. The change of the dynamics can be evaluated by internal representation of the network. This method may be useful for medical diagnoses.","PeriodicalId":156581,"journal":{"name":"Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"System identification of a circadian signal source using BP neural networks\",\"authors\":\"Y. Cisse, Y. Kinouchi, H. Nagashino, M. Akutagawa\",\"doi\":\"10.1109/IEMBS.1998.746130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The dynamics of some biological signal sources may be identified through measured data. The regulating characteristics of the wake-sleep circadian rhythm is identified here as an example by using BP neural networks. As a result, a MA model of neural networks can acquire the characteristics. The change of wake-sleep period is almost controlled suppressively by the data of preceding three days. The change of the dynamics can be evaluated by internal representation of the network. This method may be useful for medical diagnoses.\",\"PeriodicalId\":156581,\"journal\":{\"name\":\"Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1998.746130\",\"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 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1998.746130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
System identification of a circadian signal source using BP neural networks
The dynamics of some biological signal sources may be identified through measured data. The regulating characteristics of the wake-sleep circadian rhythm is identified here as an example by using BP neural networks. As a result, a MA model of neural networks can acquire the characteristics. The change of wake-sleep period is almost controlled suppressively by the data of preceding three days. The change of the dynamics can be evaluated by internal representation of the network. This method may be useful for medical diagnoses.