{"title":"基于模糊知识库的快速推理神经网络","authors":"K. S. Kumar, M. Sparancia, A. Unnikrishnan","doi":"10.1109/CMPSAC.1992.217577","DOIUrl":null,"url":null,"abstract":"The authors discuss a framework where fast inferences could be made from a fuzzy knowledge base of examples using neural networks. They sketch a scheme for defuzzifying the example base into a set of similarity vectors using a trait-adjusted similarity measure between a pair of fuzzy terms, and then training a neural network with these similarity vectors. A multilayer feedforward neural network with backpropagation learning rule was used.<<ETX>>","PeriodicalId":286518,"journal":{"name":"[1992] Proceedings. The Sixteenth Annual International Computer Software and Applications Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A neural network for fast inferencing on a fuzzy knowledge base\",\"authors\":\"K. S. Kumar, M. Sparancia, A. Unnikrishnan\",\"doi\":\"10.1109/CMPSAC.1992.217577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors discuss a framework where fast inferences could be made from a fuzzy knowledge base of examples using neural networks. They sketch a scheme for defuzzifying the example base into a set of similarity vectors using a trait-adjusted similarity measure between a pair of fuzzy terms, and then training a neural network with these similarity vectors. A multilayer feedforward neural network with backpropagation learning rule was used.<<ETX>>\",\"PeriodicalId\":286518,\"journal\":{\"name\":\"[1992] Proceedings. The Sixteenth Annual International Computer Software and Applications Conference\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] Proceedings. The Sixteenth Annual International Computer Software and Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CMPSAC.1992.217577\",\"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. The Sixteenth Annual International Computer Software and Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMPSAC.1992.217577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neural network for fast inferencing on a fuzzy knowledge base
The authors discuss a framework where fast inferences could be made from a fuzzy knowledge base of examples using neural networks. They sketch a scheme for defuzzifying the example base into a set of similarity vectors using a trait-adjusted similarity measure between a pair of fuzzy terms, and then training a neural network with these similarity vectors. A multilayer feedforward neural network with backpropagation learning rule was used.<>
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