Neural network signal understanding for instrumentation

L. F. Pau, F. S. Johansen

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data
Original languageEnglish
JournalI E E E Transactions on Instrumentation and Measurement
Volume39
Issue number4
Pages (from-to)558-564
ISSN0018-9456
DOIs
Publication statusPublished - 1990

Bibliographical note

Copyright: 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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