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Using Neural Networks for Estimating Cruise Missile Reliability (Paperback) Loot Price: R1,348
Discovery Miles 13 480
Using Neural Networks for Estimating Cruise Missile Reliability (Paperback): Donald L. Hoffman

Using Neural Networks for Estimating Cruise Missile Reliability (Paperback)

Donald L. Hoffman

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Loot Price R1,348 Discovery Miles 13 480 | Repayment Terms: R126 pm x 12*

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ACC believes its current methodology for predicting the reliability of its Air Launched Cruise Missile (ALCM) and Advanced Cruise Missile (ACM) stockpiles could be improved. They require a predictive model that delivers the best possible 24-month projection of cruise missile reliability using existing data sources, collection methods and software. It should be easily maintainable and developed to allow a layperson to enter updated data and receive an accurate reliability prediction. The focus of this thesis is to improve upon free flight reliability, although the techniques could also be applied to the captive carry portion of the missile reliability equation. The following steps were taken to ensure maximum accuracy in model results. 1. Add more detail to flight test reliability calculation. 2. Convert the ground test data into a usable form (reduce). 3. Engage in an exercise in feature selection. 4. Develop a Matlab model prototype. 5. Validate the model via problems with known solutions. 6. Apply an appropriate data fusion technique to the different network outputs (logistic regression, feed-forward and radial basis function). 7. Put the model into the form of a usable tool for the end-user. The end product is the ALCM/ACM Reliability Estimation System (AARES), a VBA-based model that meets all user criteria.

General

Imprint: Biblioscholar
Country of origin: United States
Release date: November 2012
First published: November 2012
Authors: Donald L. Hoffman
Dimensions: 246 x 189 x 7mm (L x W x T)
Format: Paperback - Trade
Pages: 124
ISBN-13: 978-1-288-33212-0
Categories: Books > Social sciences > Education > General
LSN: 1-288-33212-2
Barcode: 9781288332120

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