{"title":"评估人工神经网络的可靠性","authors":"G. Bolt","doi":"10.1109/IJCNN.1991.170462","DOIUrl":null,"url":null,"abstract":"The complex problem of assessing the reliability of a neural network is addressed. This is approached by first examining the style in which neural networks fail, and it is concluded that a continuous measure is required. Various factors are identified which will influence the definition of such a reliability measure. For various situations, examples are given of suitable reliability measures for the multilayer perceptron. An assessment strategy for a neural network's reliability is also developed. Two conventional methods are discussed (fault injection and mean-time-before-failure), and certain deficiencies are noted. From this, a more suitable service degradation method is developed. The importance of choosing a reasonable timescale for a simulation environment is also discussed. Examples of each style of simulation method are given for the multilayer perceptron.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Assessing the reliability of artificial neural networks\",\"authors\":\"G. Bolt\",\"doi\":\"10.1109/IJCNN.1991.170462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The complex problem of assessing the reliability of a neural network is addressed. This is approached by first examining the style in which neural networks fail, and it is concluded that a continuous measure is required. Various factors are identified which will influence the definition of such a reliability measure. For various situations, examples are given of suitable reliability measures for the multilayer perceptron. An assessment strategy for a neural network's reliability is also developed. Two conventional methods are discussed (fault injection and mean-time-before-failure), and certain deficiencies are noted. From this, a more suitable service degradation method is developed. The importance of choosing a reasonable timescale for a simulation environment is also discussed. Examples of each style of simulation method are given for the multilayer perceptron.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170462\",\"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] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessing the reliability of artificial neural networks
The complex problem of assessing the reliability of a neural network is addressed. This is approached by first examining the style in which neural networks fail, and it is concluded that a continuous measure is required. Various factors are identified which will influence the definition of such a reliability measure. For various situations, examples are given of suitable reliability measures for the multilayer perceptron. An assessment strategy for a neural network's reliability is also developed. Two conventional methods are discussed (fault injection and mean-time-before-failure), and certain deficiencies are noted. From this, a more suitable service degradation method is developed. The importance of choosing a reasonable timescale for a simulation environment is also discussed. Examples of each style of simulation method are given for the multilayer perceptron.<>