{"title":"The designing and training of a fuzzy neural Hamming classifier","authors":"Q. Hua, Q.-L. Zhen","doi":"10.1109/ISMVL.2001.924595","DOIUrl":null,"url":null,"abstract":"The Fuzzy Neural Hamming Classifier (FNHC) can resolve the pattern overlap with the degree of fuzzy class membership; ensure the convergence and decrease the interconnection with the comparison subnet; accept both binary and non-binary input. Using only integer threshold and weights, FNHC is easily implemented in VLSI technology.","PeriodicalId":297353,"journal":{"name":"Proceedings 31st IEEE International Symposium on Multiple-Valued Logic","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 31st IEEE International Symposium on Multiple-Valued Logic","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMVL.2001.924595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Fuzzy Neural Hamming Classifier (FNHC) can resolve the pattern overlap with the degree of fuzzy class membership; ensure the convergence and decrease the interconnection with the comparison subnet; accept both binary and non-binary input. Using only integer threshold and weights, FNHC is easily implemented in VLSI technology.
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