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dc.contributor.authorDionisi, Steven M.
dc.date.accessioned2016-06-06T17:45:47Z
dc.date.available2016-06-06T17:45:47Z
dc.date.issued1993-10
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/611856
dc.descriptionInternational Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevadaen_US
dc.description.abstractAn artificial neural network (ANN) for use in real time data reduction and analysis will be presented. The use and advantage of hardware and software implementations of neural networks will be considered. The ability of neural networks to learn and store associations between different sets of data can be used to create custom algorithms for some of the data analysis done during missions. Once trained, the ANN can distill the signals from several sensors into a single output, such as safe/unsafe. Used on a neural chip, the trained ANN can eliminate the need for A/D conversions and multiplexing for processing of combined parameters and the massively parallel nature of the network allows the processing time to remain independent of the number of parameters. As a software routine, the advantages of using an ANN over conventional algorithms include the ease of use for engineers, and the ability to handle nonlinear, noisy and imperfect data. This paper will apply the ANN to performance data from a T-38 aircraft.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © International Foundation for Telemeteringen
dc.subjectNeural Networksen
dc.subjectReal Time Data Analysisen
dc.subjectMassively Parallel Systemsen
dc.titleReal Time Data Reduction and Analysis Using Artificial Neural Networksen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentAFFTCen
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-09-11T11:53:24Z
html.description.abstractAn artificial neural network (ANN) for use in real time data reduction and analysis will be presented. The use and advantage of hardware and software implementations of neural networks will be considered. The ability of neural networks to learn and store associations between different sets of data can be used to create custom algorithms for some of the data analysis done during missions. Once trained, the ANN can distill the signals from several sensors into a single output, such as safe/unsafe. Used on a neural chip, the trained ANN can eliminate the need for A/D conversions and multiplexing for processing of combined parameters and the massively parallel nature of the network allows the processing time to remain independent of the number of parameters. As a software routine, the advantages of using an ANN over conventional algorithms include the ease of use for engineers, and the ability to handle nonlinear, noisy and imperfect data. This paper will apply the ANN to performance data from a T-38 aircraft.


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