{"title":"多层前馈神经网络的快速学习算法","authors":"M. Liang, Shi-xi Wang, Youqing Luo","doi":"10.1109/NAECON.1994.332959","DOIUrl":null,"url":null,"abstract":"In this paper, the problem of fast learning algorithm for multi-layered feedforward neural network (MLFNN) is discussed. A new fast backpropagation (FB-P) learning algorithm is proposed, By the analysis of FB-P learning algorithm, a modified FB-P (MFB-P) learning algorithm is presented. Simulations are run with the problem of XOR for B-P, FB-P and MFB-P, and the corresponding results indicate that MFB-P or FB-P converges much more quickly than B-P and MFB-P has much better generalization than FB-P or B-P.<<ETX>>","PeriodicalId":281754,"journal":{"name":"Proceedings of National Aerospace and Electronics Conference (NAECON'94)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Fast learning algorithms for multi-layered feedforward neural network\",\"authors\":\"M. Liang, Shi-xi Wang, Youqing Luo\",\"doi\":\"10.1109/NAECON.1994.332959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the problem of fast learning algorithm for multi-layered feedforward neural network (MLFNN) is discussed. A new fast backpropagation (FB-P) learning algorithm is proposed, By the analysis of FB-P learning algorithm, a modified FB-P (MFB-P) learning algorithm is presented. Simulations are run with the problem of XOR for B-P, FB-P and MFB-P, and the corresponding results indicate that MFB-P or FB-P converges much more quickly than B-P and MFB-P has much better generalization than FB-P or B-P.<<ETX>>\",\"PeriodicalId\":281754,\"journal\":{\"name\":\"Proceedings of National Aerospace and Electronics Conference (NAECON'94)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of National Aerospace and Electronics Conference (NAECON'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAECON.1994.332959\",\"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 National Aerospace and Electronics Conference (NAECON'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.1994.332959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast learning algorithms for multi-layered feedforward neural network
In this paper, the problem of fast learning algorithm for multi-layered feedforward neural network (MLFNN) is discussed. A new fast backpropagation (FB-P) learning algorithm is proposed, By the analysis of FB-P learning algorithm, a modified FB-P (MFB-P) learning algorithm is presented. Simulations are run with the problem of XOR for B-P, FB-P and MFB-P, and the corresponding results indicate that MFB-P or FB-P converges much more quickly than B-P and MFB-P has much better generalization than FB-P or B-P.<>
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