Title :
A neural network for evaluating diagnostic evidence
Author :
Sheppard, John W. ; Simpson, William R.
Author_Institution :
ARINC Res. Corp., Annapolis, MD, USA
Abstract :
The authors present the results of developing a trained neural network that is embedded in a portable maintenance aid called POINTER (Portable Interactive Troubleshooter). This neural network is model-independent and may be used on a wide class of diagnostic problems. The authors review, the process by which they selected the network paradigm, gathered the training and testing data set (with a discussion on the uncertainty formulation), trained and tested the network, and incorporated the resulting network into POINTER. They also discuss the overall architecture for reasoning under uncertainty as implemented in POINTER
Keywords :
automatic test equipment; knowledge based systems; learning systems; maintenance engineering; neural nets; Bayes probability; POINTER; Portable Interactive Troubleshooter; architecture; diagnostic evidence evaluation; neural network; portable maintenance aid; reasoning under uncertainty; uncertainty formulation; Built-in self-test; Electrohydraulics; Neural networks; Pattern recognition; Performance evaluation; Prototypes; System testing; Test equipment; Uncertainty; Weapons;
Conference_Titel :
Aerospace and Electronics Conference, 1991. NAECON 1991., Proceedings of the IEEE 1991 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-0085-8
DOI :
10.1109/NAECON.1991.165831
