{"title":"模糊神经计算:一些进展","authors":"M. Gupta","doi":"10.1109/ICIT.2000.854113","DOIUrl":null,"url":null,"abstract":"In this paper we give some basic principles of fuzzy neural computing using synaptic and somatic operations. We first briefly review the neural systems based upon the conventional algebraic synaptic (confluence) and somatic (aggregation) operations. Then we provide a detailed neuronal morphology based upon fuzzy logic and its generalization in the form of T-operators. For such fuzzy logic based neurons, we then develop the learning and adaptation algorithm.","PeriodicalId":405648,"journal":{"name":"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fuzzy-neural computing: some advances\",\"authors\":\"M. Gupta\",\"doi\":\"10.1109/ICIT.2000.854113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we give some basic principles of fuzzy neural computing using synaptic and somatic operations. We first briefly review the neural systems based upon the conventional algebraic synaptic (confluence) and somatic (aggregation) operations. Then we provide a detailed neuronal morphology based upon fuzzy logic and its generalization in the form of T-operators. For such fuzzy logic based neurons, we then develop the learning and adaptation algorithm.\",\"PeriodicalId\":405648,\"journal\":{\"name\":\"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2000.854113\",\"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 of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2000.854113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we give some basic principles of fuzzy neural computing using synaptic and somatic operations. We first briefly review the neural systems based upon the conventional algebraic synaptic (confluence) and somatic (aggregation) operations. Then we provide a detailed neuronal morphology based upon fuzzy logic and its generalization in the form of T-operators. For such fuzzy logic based neurons, we then develop the learning and adaptation algorithm.
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