Inversion of Uniform Field Eddy Current Data Using Neural Networks Mann, J. Schmerr, L. Moulder, J. Kubovich, M. 2018-02-14T03:30:29.000 2020-06-30T06:37:41Z 2020-06-30T06:37:41Z Mon Jan 01 00:00:00 UTC 1990 1990
dc.description.abstract <p>A resurgence of research in artificial neural networks has sparked interest in applying these networks to difficult computational tasks such as inversion. Artificial neural networks are composed of simple processing elements, richly interconnected. These networks can be trained to perform arbitrary mappings between sets of input-output pairs by adjusting the weights of interconnections. They require no a priori information or built-in rules; rather, they acquire knowledge through the presentation of examples. This characteristic allows neural networks to approximate mappings for functions which do not appear to have a clearly defined algorithm or theory. Neural network performance has proven robust when faced with incomplete, fuzzy, or novel data.</p>
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dc.identifier archive/
dc.identifier.articleid 1559
dc.identifier.contextkey 5777079
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1990/allcontent/85
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/|||Sat Jan 15 02:12:15 UTC 2022
dc.source.uri 10.1007/978-1-4684-5772-8_85
dc.subject.disciplines Electromagnetics and Photonics
dc.subject.disciplines Signal Processing
dc.subject.keywords CNDE
dc.title Inversion of Uniform Field Eddy Current Data Using Neural Networks
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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