{"title":"基于粗糙集的反传播神经网络优化","authors":"Qing Shao","doi":"10.1109/MVHI.2010.204","DOIUrl":null,"url":null,"abstract":"The Couner-Propagation neural networks is weak in convergent speed, will easily sink into local minimum, and its choices of initial weights and thresholds lack sound basis. So, a new optimal algorithm of neural network based on rough set was proposed. The new approach integrates the advantages of the two algorithms; it has good understandability, simple computation and exact accuracy. Then a new algorithm based rough set was put forward and used to optimize the design of neural network weights and threshold. The results of simulation show: the new algorithm can get over the insufficiency of CP, and compared with CP, greatly improve the convergent accuracy and speed, and get a good measurement result.","PeriodicalId":34860,"journal":{"name":"HumanMachine Communication Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Couner-Propagation Neural Networks Optimization Based on Rough Set\",\"authors\":\"Qing Shao\",\"doi\":\"10.1109/MVHI.2010.204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Couner-Propagation neural networks is weak in convergent speed, will easily sink into local minimum, and its choices of initial weights and thresholds lack sound basis. So, a new optimal algorithm of neural network based on rough set was proposed. The new approach integrates the advantages of the two algorithms; it has good understandability, simple computation and exact accuracy. Then a new algorithm based rough set was put forward and used to optimize the design of neural network weights and threshold. The results of simulation show: the new algorithm can get over the insufficiency of CP, and compared with CP, greatly improve the convergent accuracy and speed, and get a good measurement result.\",\"PeriodicalId\":34860,\"journal\":{\"name\":\"HumanMachine Communication Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HumanMachine Communication Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVHI.2010.204\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HumanMachine Communication Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVHI.2010.204","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
Couner-Propagation Neural Networks Optimization Based on Rough Set
The Couner-Propagation neural networks is weak in convergent speed, will easily sink into local minimum, and its choices of initial weights and thresholds lack sound basis. So, a new optimal algorithm of neural network based on rough set was proposed. The new approach integrates the advantages of the two algorithms; it has good understandability, simple computation and exact accuracy. Then a new algorithm based rough set was put forward and used to optimize the design of neural network weights and threshold. The results of simulation show: the new algorithm can get over the insufficiency of CP, and compared with CP, greatly improve the convergent accuracy and speed, and get a good measurement result.