{"title":"多层感知器神经网络中隐藏神经元的动态失活","authors":"H. Amin, K. M. Curtis, B. Hayes-Gill","doi":"10.1109/ICECS.1996.582807","DOIUrl":null,"url":null,"abstract":"In this paper we present an approach that terminates the processing of hidden nodes, within a multilayer perceptron (MLP) neural network, if they become inactive during the learning process. The determination of the activity and non-activity of hidden nodes are based on the mean deviation of changes in the average derivative of the hidden nodes within an interval of several iterations. A decreasing threshold value is used to evaluate the mean deviation and hence to deactivate the hidden nodes accordingly.","PeriodicalId":402369,"journal":{"name":"Proceedings of Third International Conference on Electronics, Circuits, and Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamically deactivating hidden neurons in a multilayer perceptron neural network\",\"authors\":\"H. Amin, K. M. Curtis, B. Hayes-Gill\",\"doi\":\"10.1109/ICECS.1996.582807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present an approach that terminates the processing of hidden nodes, within a multilayer perceptron (MLP) neural network, if they become inactive during the learning process. The determination of the activity and non-activity of hidden nodes are based on the mean deviation of changes in the average derivative of the hidden nodes within an interval of several iterations. A decreasing threshold value is used to evaluate the mean deviation and hence to deactivate the hidden nodes accordingly.\",\"PeriodicalId\":402369,\"journal\":{\"name\":\"Proceedings of Third International Conference on Electronics, Circuits, and Systems\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Third International Conference on Electronics, Circuits, and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECS.1996.582807\",\"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 Third International Conference on Electronics, Circuits, and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECS.1996.582807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamically deactivating hidden neurons in a multilayer perceptron neural network
In this paper we present an approach that terminates the processing of hidden nodes, within a multilayer perceptron (MLP) neural network, if they become inactive during the learning process. The determination of the activity and non-activity of hidden nodes are based on the mean deviation of changes in the average derivative of the hidden nodes within an interval of several iterations. A decreasing threshold value is used to evaluate the mean deviation and hence to deactivate the hidden nodes accordingly.
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