One of the key factors which limits the use of neural networks in many industrial applica\u00adtions has been the difficulty of demonstrating that a trained network will continue to generate reli\u00adable outputs once it is in routine use. An import\u00adant potential source of errors is novel input data; that is, input data which differ significantly from the data used to train the network. The author investigates the relationship between the degree of novelty of input data and the corresponding reliability of the outputs from the network. He describes a quantitative procedure for assessing novelty, and demonstrates its performance by using an application which involves monitoring oil flow in multiphase pipelines.<\/p>\n","protected":false},"excerpt":{"rendered":"
One of the key factors which limits the use of neural networks in many industrial applica\u00adtions has been the difficulty of demonstrating that a trained network will continue to generate reli\u00adable outputs once it is in routine use. An import\u00adant potential source of errors is novel input data; that is, input data which differ significantly […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-232114","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"IEE Proceedings: Vision, Image and Signal Processing. 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