Journal article
Neural network signal understanding for instrumentation
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
Language: | English |
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Publisher: | IEEE |
Year: | 1990 |
Pages: | 558-564 |
ISSN: | 15579662 and 00189456 |
Types: | Journal article |
DOI: | 10.1109/19.57233 |
Calibration Hidden Markov models Instruments Neural networks Noise robustness Shape control Shape measurement Signal analysis Signal generators Signal processing calibration classification rates diagnosis explanation facility functional link nets instrumentation neural control neural net training neural nets neural network signal understanding tuning neural signal noise pattern recognition signal interpretation