{"title":"用神经网络建模故障间时间序列","authors":"S. Zaidi, S. N. Danial, B. Usmani","doi":"10.1109/INMIC.2008.4777772","DOIUrl":null,"url":null,"abstract":"Software inter-failure time series analysis has always been a question mark for the reliability engineers. Many models have been proposed since the problem of reliability discovers, but none of them produces adequate results. This study presents a neural network perspective of modeling inter-failure time of software. We compare different parametric models of software reliability with our proposed neural network model and found the proposed more suitable.","PeriodicalId":112530,"journal":{"name":"2008 IEEE International Multitopic Conference","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Modeling inter-failure time series using neural networks\",\"authors\":\"S. Zaidi, S. N. Danial, B. Usmani\",\"doi\":\"10.1109/INMIC.2008.4777772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software inter-failure time series analysis has always been a question mark for the reliability engineers. Many models have been proposed since the problem of reliability discovers, but none of them produces adequate results. This study presents a neural network perspective of modeling inter-failure time of software. We compare different parametric models of software reliability with our proposed neural network model and found the proposed more suitable.\",\"PeriodicalId\":112530,\"journal\":{\"name\":\"2008 IEEE International Multitopic Conference\",\"volume\":\"126 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Multitopic Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INMIC.2008.4777772\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Multitopic Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INMIC.2008.4777772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling inter-failure time series using neural networks
Software inter-failure time series analysis has always been a question mark for the reliability engineers. Many models have been proposed since the problem of reliability discovers, but none of them produces adequate results. This study presents a neural network perspective of modeling inter-failure time of software. We compare different parametric models of software reliability with our proposed neural network model and found the proposed more suitable.