{"title":"反向传播的模糊控制","authors":"P. Arabshahi, J.J. Choi, R. Marks, T. P. Caudell","doi":"10.1109/FUZZY.1992.258787","DOIUrl":null,"url":null,"abstract":"The authors propose a fuzzy logic controlled implementation of the backpropagation training algorithm for layered perceptrons. The heuristics for adjusting the value of the learning rate eta are incorporated into a simple fuzzy control system. This provides automatic tuning of the learning rate parameter depending on the shape of the error surface. The application of this straightforward procedure was shown to be able to dramatically improve training time in some problems.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"Fuzzy control of backpropagation\",\"authors\":\"P. Arabshahi, J.J. Choi, R. Marks, T. P. Caudell\",\"doi\":\"10.1109/FUZZY.1992.258787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors propose a fuzzy logic controlled implementation of the backpropagation training algorithm for layered perceptrons. The heuristics for adjusting the value of the learning rate eta are incorporated into a simple fuzzy control system. This provides automatic tuning of the learning rate parameter depending on the shape of the error surface. The application of this straightforward procedure was shown to be able to dramatically improve training time in some problems.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258787\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1992.258787","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors propose a fuzzy logic controlled implementation of the backpropagation training algorithm for layered perceptrons. The heuristics for adjusting the value of the learning rate eta are incorporated into a simple fuzzy control system. This provides automatic tuning of the learning rate parameter depending on the shape of the error surface. The application of this straightforward procedure was shown to be able to dramatically improve training time in some problems.<>
![[1992 Proceedings] IEEE International Conference on Fuzzy Systems](/Content/css/sci/img/zetp.jpg)
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
