{"title":"控制系统中的神经网络","authors":"S. Saksida, F. Bremsak","doi":"10.1109/MELCON.1991.161966","DOIUrl":null,"url":null,"abstract":"A modified backpropagation algorithm with a linear output function is discussed from the viewpoint of its convenience in control tasks. Two methods are presented for teaching neural networks to act as an inverse plant. In the simulation example a discrete integrator was used as the plant. The best results were obtained by a combination of both methods.<<ETX>>","PeriodicalId":193917,"journal":{"name":"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural networks in control systems\",\"authors\":\"S. Saksida, F. Bremsak\",\"doi\":\"10.1109/MELCON.1991.161966\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A modified backpropagation algorithm with a linear output function is discussed from the viewpoint of its convenience in control tasks. Two methods are presented for teaching neural networks to act as an inverse plant. In the simulation example a discrete integrator was used as the plant. The best results were obtained by a combination of both methods.<<ETX>>\",\"PeriodicalId\":193917,\"journal\":{\"name\":\"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MELCON.1991.161966\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MELCON.1991.161966","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A modified backpropagation algorithm with a linear output function is discussed from the viewpoint of its convenience in control tasks. Two methods are presented for teaching neural networks to act as an inverse plant. In the simulation example a discrete integrator was used as the plant. The best results were obtained by a combination of both methods.<>