{"title":"自我改进的联想神经网络模型","authors":"Tao Wang, X. Zhuang, X. Xing","doi":"10.1109/IJCNN.1991.170384","DOIUrl":null,"url":null,"abstract":"A self-improving associative neural network (SIANN) model is presented. The implementation of this neural network consists of two phases, namely a learning procedure and a retrieval procedure. The learning procedure that determines connection weights among the neurons provides the ability to embody certain regularities implicit in a noisy pattern. It can be realized by a multilayer logic neural network using one pass. The self-improvement of the noisy pattern is achieved by the retrieval procedure. The salient points of the neural network model result from the fact that it does not require a set of training patterns, uses only one pass for the learning procedure, and converges very quickly. Computer experimental results illustrate the self-improvement of the neural network.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-improving associative neural network models\",\"authors\":\"Tao Wang, X. Zhuang, X. Xing\",\"doi\":\"10.1109/IJCNN.1991.170384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A self-improving associative neural network (SIANN) model is presented. The implementation of this neural network consists of two phases, namely a learning procedure and a retrieval procedure. The learning procedure that determines connection weights among the neurons provides the ability to embody certain regularities implicit in a noisy pattern. It can be realized by a multilayer logic neural network using one pass. The self-improvement of the noisy pattern is achieved by the retrieval procedure. The salient points of the neural network model result from the fact that it does not require a set of training patterns, uses only one pass for the learning procedure, and converges very quickly. Computer experimental results illustrate the self-improvement of the neural network.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170384\",\"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] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A self-improving associative neural network (SIANN) model is presented. The implementation of this neural network consists of two phases, namely a learning procedure and a retrieval procedure. The learning procedure that determines connection weights among the neurons provides the ability to embody certain regularities implicit in a noisy pattern. It can be realized by a multilayer logic neural network using one pass. The self-improvement of the noisy pattern is achieved by the retrieval procedure. The salient points of the neural network model result from the fact that it does not require a set of training patterns, uses only one pass for the learning procedure, and converges very quickly. Computer experimental results illustrate the self-improvement of the neural network.<>