Accession Number : ADA293114
Title : Neural Network False Alarm Filter. Volume 2.
Descriptive Note : Final rept. Sep 92-Sep 94,
Corporate Author : RAYTHEON CO TEWKSBURY MA MISSILE SYSTEMS DIV
Personal Author(s) : Aylstock, F. ; Elerin, L. ; Hintz, J. ; Learoyd, C. ; Press, R.
PDF Url : ADA293114
Report Date : DEC 1994
Pagination or Media Count : 91
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.
Descriptors : *NEURAL NETS, *FALSE ALARMS, *FILTERS, TEST AND EVALUATION, ALGORITHMS, REQUIREMENTS, METHODOLOGY, VIBRATION, TRAINING, REAL TIME, STATE OF THE ART, BIBLIOGRAPHIES, COMPUTER ARCHITECTURE, PHASE, COSTS, SELF CONTAINED, COMPUTER NETWORKS, LEARNING, PARITY.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE