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http://dx.doi.org/10.5762/KAIS.2020.21.6.374

Study on Improving Learning Speed of Artificial Neural Network Model for Ammunition Stockpile Reliability Classification  

Lee, Dong-Nyok (Department of Aerospace Engineering, Inha University)
Yoon, Keun-Sig (Defense Agency for Technology & Quality)
Noh, Yoo-Chan (Defense Agency for Technology & Quality)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.6, 2020 , pp. 374-382 More about this Journal
Abstract
The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.
Keywords
Artificial Neural Network; Ammunition; Reliability; Normalization; Classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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