{"title":"自适应神经网络研究进展","authors":"R. Palnitkar, J. Cannady","doi":"10.1109/SECON.2004.1287896","DOIUrl":null,"url":null,"abstract":"Artificial neural networks are inspired from their biological counterparts. Adaptation is one of the most important features of both types of networks. Adaptive artificial neural networks are a class of networks used in dynamic environments. They are characterized by online learning. A number of techniques are used to provide adaptability to neural networks: adaptation by weight modification, by neuronal property modification, and by network structure modification. A brief review of various types of implementations is provided.","PeriodicalId":324953,"journal":{"name":"IEEE SoutheastCon, 2004. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A Review of Adaptive Neural Networks\",\"authors\":\"R. Palnitkar, J. Cannady\",\"doi\":\"10.1109/SECON.2004.1287896\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial neural networks are inspired from their biological counterparts. Adaptation is one of the most important features of both types of networks. Adaptive artificial neural networks are a class of networks used in dynamic environments. They are characterized by online learning. A number of techniques are used to provide adaptability to neural networks: adaptation by weight modification, by neuronal property modification, and by network structure modification. A brief review of various types of implementations is provided.\",\"PeriodicalId\":324953,\"journal\":{\"name\":\"IEEE SoutheastCon, 2004. Proceedings.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE SoutheastCon, 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SECON.2004.1287896\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE SoutheastCon, 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2004.1287896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial neural networks are inspired from their biological counterparts. Adaptation is one of the most important features of both types of networks. Adaptive artificial neural networks are a class of networks used in dynamic environments. They are characterized by online learning. A number of techniques are used to provide adaptability to neural networks: adaptation by weight modification, by neuronal property modification, and by network structure modification. A brief review of various types of implementations is provided.