{"title":"神经网络非线性预测器","authors":"A. Ukrainec, S. Haykin, J. McGregor","doi":"10.1109/IJCNN.1989.118507","DOIUrl":null,"url":null,"abstract":"Summary form only given, as follows. The authors demonstrate that a backpropagation neural network can be used for nonlinear time series prediction. In a computer experiment an example nonlinear time series is used to teach a network the necessary mapping in a supervised manner. Predictor learning curves are presented, showing successful operation. Improvements to the neural network structure with regard to the reduction of the observed performance deficit are discussed.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"39 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A neural network nonlinear predictor\",\"authors\":\"A. Ukrainec, S. Haykin, J. McGregor\",\"doi\":\"10.1109/IJCNN.1989.118507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given, as follows. The authors demonstrate that a backpropagation neural network can be used for nonlinear time series prediction. In a computer experiment an example nonlinear time series is used to teach a network the necessary mapping in a supervised manner. Predictor learning curves are presented, showing successful operation. Improvements to the neural network structure with regard to the reduction of the observed performance deficit are discussed.<<ETX>>\",\"PeriodicalId\":199877,\"journal\":{\"name\":\"International 1989 Joint Conference on Neural Networks\",\"volume\":\"39 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International 1989 Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1989.118507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International 1989 Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1989.118507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Summary form only given, as follows. The authors demonstrate that a backpropagation neural network can be used for nonlinear time series prediction. In a computer experiment an example nonlinear time series is used to teach a network the necessary mapping in a supervised manner. Predictor learning curves are presented, showing successful operation. Improvements to the neural network structure with regard to the reduction of the observed performance deficit are discussed.<>