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Proceedings Paper

On-line fault detection using integrated neural networks
Author(s): Jay Lee; John Tsai
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Paper Abstract

A practical neural networks paradigm is described for material handling. The learning algorithm is a modification of the cerebella model articulation controller (CMAC) developed by Albus. A table look-up approach detects faults by monitoring the output patterns from sensors and actuators. By analyzing the timing sequence, abnormal conditions can be detected. CMAC offers an alternative to conventional back-propagated, multilayered networks, with the advantage of rapid convergence. The approach appears to be more efficient for the on-line and real-time applications required in automated systems.

Paper Details

Date Published: 16 September 1992
PDF: 11 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140021
Show Author Affiliations
Jay Lee, National Science Foundation (United States)
John Tsai, Arthur D. Little, Inc. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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