{"title":"利用遗传算法估算软件工作量","authors":"B. MariKumar, P. Latha, E. Praynlin","doi":"10.18000/IJISAC.50142","DOIUrl":null,"url":null,"abstract":"Mari Kumar B1, Dr. Latha P2, Praynlin E3 1,3Government College of Engineering, Tirunelveli, India 2 Department of Computer Science and Engineering, Anna University 1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com Abstract A feed forward back propagation neural network is most commonly used to the form of artificial neural network. This algorithm being a correct procedure, it accurate result in the neural network. The estimate of this method as the training of Neural Network is compared with that of genetic algorithm, that the form of based on estimate the software effort estimation. The comparison of two methods is used to accuracy of the software effort estimation.","PeriodicalId":121456,"journal":{"name":"International Journal on Information Sciences and Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SOFTWARE EFFORT ESTIMATION USING GENETIC ALGORITHM\",\"authors\":\"B. MariKumar, P. Latha, E. Praynlin\",\"doi\":\"10.18000/IJISAC.50142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mari Kumar B1, Dr. Latha P2, Praynlin E3 1,3Government College of Engineering, Tirunelveli, India 2 Department of Computer Science and Engineering, Anna University 1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com Abstract A feed forward back propagation neural network is most commonly used to the form of artificial neural network. This algorithm being a correct procedure, it accurate result in the neural network. The estimate of this method as the training of Neural Network is compared with that of genetic algorithm, that the form of based on estimate the software effort estimation. The comparison of two methods is used to accuracy of the software effort estimation.\",\"PeriodicalId\":121456,\"journal\":{\"name\":\"International Journal on Information Sciences and Computing\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal on Information Sciences and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18000/IJISAC.50142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Information Sciences and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18000/IJISAC.50142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
Mari Kumar B1，博士Latha P2, Praynlin E3 1,3印度Tirunelveli政府工程学院2安娜大学计算机科学与工程系1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com摘要前馈-反传播神经网络是人工神经网络中最常用的一种形式。该算法是一个正确的过程，在神经网络中得到了准确的结果。将该方法作为神经网络训练的估计方法与遗传算法的估计方法进行了比较，发现基于估计的形式是基于软件工作量估计。通过对两种方法的比较，提高了软件工作量估算的准确性。
SOFTWARE EFFORT ESTIMATION USING GENETIC ALGORITHM
Mari Kumar B1, Dr. Latha P2, Praynlin E3 1,3Government College of Engineering, Tirunelveli, India 2 Department of Computer Science and Engineering, Anna University 1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com Abstract A feed forward back propagation neural network is most commonly used to the form of artificial neural network. This algorithm being a correct procedure, it accurate result in the neural network. The estimate of this method as the training of Neural Network is compared with that of genetic algorithm, that the form of based on estimate the software effort estimation. The comparison of two methods is used to accuracy of the software effort estimation.
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