Neural Network False Alarm Filter. Volume 2.
Abstract
This effort identified, developed and demonstrated a set of approaches for applying neural network learning techniques to the development of a real-time built-in test (BIT) capability to filter out false-alarms from the BIT output. Following a state-of-the-art assessment, a decision space of 19 neural network models, 9 fault report causes and 12 common groups of BIT techniques was identified. From this space, 4 unique, high-potential combinations were selected for further investigation. These techniques were subsequently simulated for application to a MILSATCOM system. Detailed analyses of their strengths and weaknesses were performed along with cost/benefit analyses. This study concluded that the best candidates for neural network insertion are new systems where neural network requirements can be included in the initial system design and that a major challenge is the availability or real data for training of the networks. Volume I of this report documents the activities and findings of the effort, including an extensive, annotated bibliography. Volume II contains a tutorial overview of the neural networks, BIT techniques and false alarm causes utilized in the final phases of this study.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1994
- Accession Number
- ADA293114
Entities
People
- C. Learoyd
- F. Aylstock
- J. Hintz
- L. Elerin
- R. Press