Neutral networks for control, identification and diagnosis

Details Neutral networks for control, identification and diagnosis Neutral networks for control, identification and diagnosis

Nov 1994

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994adspr..14..297r&link_type=abstract

Advances in Space Research, Volume 14, Issue 11, p. 297-305.

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Advances in the theory and technology of artificial neural networks provide the potential for new approaches to the problems of control, identification, and diagnosis for large, complex systems. However, these approaches must be validated for specific applications before they can be exploited effectively. Because of the unique capabilities they offer, neural networks should play an important role in space exploration systems operations. After a brief introduction to neural networks is presented, some applications of neural networks to identification and control of space systems are described and discussed. They span the spectrum of relatively straightforward to rather complex applications. An explanation of how neural networks can be applied to such important tasks as fault diagnosis and accommodation is presented. Neural networks are shown to be part of the hierarchy of intelligent control where a higher order decision element monitors and supervises lower order elements for sensing and actuation.

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