{"title":"TInMANN: the integer Markovian artificial neural network","authors":"E. David, den Bout, T. Miller","doi":"10.1109/IJCNN.1989.118700","DOIUrl":null,"url":null,"abstract":"A massively parallel, all-digital, stochastic digital architecture called TInMANN is described. It performs competitive and Kohonen types of learning at rates as high as 145000 training examples per second regardless of network size. Simulations of TInMANN, both with and without its conscience mechanism activated, demonstrate its effectiveness on a number of example problems.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International 1989 Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1989.118700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34
Abstract
A massively parallel, all-digital, stochastic digital architecture called TInMANN is described. It performs competitive and Kohonen types of learning at rates as high as 145000 training examples per second regardless of network size. Simulations of TInMANN, both with and without its conscience mechanism activated, demonstrate its effectiveness on a number of example problems.<>
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