T. Roppel, K. Dunman, M. Padgett, D. Wilson, T. Lindblad
{"title":"气味传感器阵列的特征级信号处理","authors":"T. Roppel, K. Dunman, M. Padgett, D. Wilson, T. Lindblad","doi":"10.1109/IECON.1997.671050","DOIUrl":null,"url":null,"abstract":"A recurrent back-propagation neural algorithm is trained to classify nine odors. The algorithm is capable of correctly identifying the odors regardless of the time sequence of presentation. The classification is performed in near-real time and is based upon the transient response of an array of 15 tin-oxide gas sensors.","PeriodicalId":404447,"journal":{"name":"Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Feature-level signal processing for odor sensor arrays\",\"authors\":\"T. Roppel, K. Dunman, M. Padgett, D. Wilson, T. Lindblad\",\"doi\":\"10.1109/IECON.1997.671050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recurrent back-propagation neural algorithm is trained to classify nine odors. The algorithm is capable of correctly identifying the odors regardless of the time sequence of presentation. The classification is performed in near-real time and is based upon the transient response of an array of 15 tin-oxide gas sensors.\",\"PeriodicalId\":404447,\"journal\":{\"name\":\"Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IECON.1997.671050\",\"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 IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON.1997.671050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature-level signal processing for odor sensor arrays
A recurrent back-propagation neural algorithm is trained to classify nine odors. The algorithm is capable of correctly identifying the odors regardless of the time sequence of presentation. The classification is performed in near-real time and is based upon the transient response of an array of 15 tin-oxide gas sensors.
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