S. Oniga, A. Tisan, D. Mic, C. Lung, I. Orha, A. Buchman, A. Vida-Ratiu
{"title":"用于智能设备开发的前馈神经网络的FPGA实现","authors":"S. Oniga, A. Tisan, D. Mic, C. Lung, I. Orha, A. Buchman, A. Vida-Ratiu","doi":"10.1109/ISSCS.2009.5206129","DOIUrl":null,"url":null,"abstract":"This paper presents the results obtained in the implementation of Feed-Forward Artificial Neural Networks (FF-ANN) with one or several layers, used in the development of smart devices that needs learning capability and adaptive behavior. The networks were implemented using ANN specific blocks created by the authors using the System Generator software. The training and the testing of the networks was conducted using sets of 150 training and test vectors with 7 elements.","PeriodicalId":277587,"journal":{"name":"2009 International Symposium on Signals, Circuits and Systems","volume":" 10","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"FPGA implementation of Feed-Forward Neural Networks for smart devices development\",\"authors\":\"S. Oniga, A. Tisan, D. Mic, C. Lung, I. Orha, A. Buchman, A. Vida-Ratiu\",\"doi\":\"10.1109/ISSCS.2009.5206129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the results obtained in the implementation of Feed-Forward Artificial Neural Networks (FF-ANN) with one or several layers, used in the development of smart devices that needs learning capability and adaptive behavior. The networks were implemented using ANN specific blocks created by the authors using the System Generator software. The training and the testing of the networks was conducted using sets of 150 training and test vectors with 7 elements.\",\"PeriodicalId\":277587,\"journal\":{\"name\":\"2009 International Symposium on Signals, Circuits and Systems\",\"volume\":\" 10\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Symposium on Signals, Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSCS.2009.5206129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Symposium on Signals, Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCS.2009.5206129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FPGA implementation of Feed-Forward Neural Networks for smart devices development
This paper presents the results obtained in the implementation of Feed-Forward Artificial Neural Networks (FF-ANN) with one or several layers, used in the development of smart devices that needs learning capability and adaptive behavior. The networks were implemented using ANN specific blocks created by the authors using the System Generator software. The training and the testing of the networks was conducted using sets of 150 training and test vectors with 7 elements.