{"title":"学习基于模糊规则的神经网络的函数逼近","authors":"C. Higgins, R. M. Goodman","doi":"10.1109/IJCNN.1992.287127","DOIUrl":null,"url":null,"abstract":"The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":"{\"title\":\"Learning fuzzy rule-based neural networks for function approximation\",\"authors\":\"C. Higgins, R. M. Goodman\",\"doi\":\"10.1109/IJCNN.1992.287127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<<ETX>>\",\"PeriodicalId\":286849,\"journal\":{\"name\":\"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1992.287127\",\"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 1992] IJCNN International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1992.287127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning fuzzy rule-based neural networks for function approximation
The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<>