{"title":"一种新的基于共轭梯度和输出权优化的神经网络算法","authors":"Y. Li, Xun Cai, M. Li","doi":"10.1109/ICNC.2011.6022056","DOIUrl":null,"url":null,"abstract":"On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"2 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Notice of RetractionA new neural network algorithm based on conjugate gradient and output weight optimization\",\"authors\":\"Y. Li, Xun Cai, M. Li\",\"doi\":\"10.1109/ICNC.2011.6022056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.\",\"PeriodicalId\":299503,\"journal\":{\"name\":\"2011 Seventh International Conference on Natural Computation\",\"volume\":\"2 6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Seventh International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2011.6022056\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Seventh International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6022056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Notice of RetractionA new neural network algorithm based on conjugate gradient and output weight optimization
On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.