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A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network
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Hong Gi Shim (The graduate school of Information Management and Security, Korea University)
Seung Kown Kim (Division of Information Management Engineering, Korea University)
Vol. 14, No. 3, Page: 25 ~ 39
Keywords
Multilayer Perceptrons(MLP), General Neural Netwowk(GRNN), Wargame Simulation, Battalion Command in Battle Training
Abstract
The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.
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인공신경망을 이용한 대대전투간 작전지속능력 예측
심홍기 (고려대학교 정보경영공학 전문대학원)
김승권 (고려대학교 정보경영공학부)
Abstract
본 연구는 인공신경망을 이용하여 대대급 방어 작전에서 임의시점에서의 작전지속능력을 예측하는 데 있다. 전투결과에 대한 수학적 모델링은 이를 위한 많은 요인들이 가지는 시?공간적 가변성으로 인해 전투력을 평가하는데 많은 문제점이 있었다. 따라서 이번 연구에서는 대대 전투지휘훈련간 각 부대의 생존률을 전방향 다층 신경망(Feed-Forward Multilayer Perceptrons, MLP)과 일반 회귀신경망(General Regression Neural Network, GRNN)모형에 적용하여 임무달성 여부를 예측하였다. 실험 결과 매개변수들의 비선형적인 관계에도 불구하고 각각 82.62%, 85.48%의 적중률을 보여 일반회귀신경망 모형이 지휘관이 상황을 인식하고 예비대 투입 우선순위 선정 등 실시간 지휘결심을 하는데 도움을 줄 수 있는 방법임을 보여준다.
Cite this article
JIIS Style
Shim, H. G., and S. K. Kim, "A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network", Journal of Intelligence and Information Systems, Vol. 14, No. 3 (2008), 25~39.

IEEE Style
Hong Gi Shim, and Seung Kown Kim, "A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network", Journal of Intelligence and Information Systems, vol. 14, no. 3, pp. 25~39, 2008.

ACM Style
Shim, H. G., and Kim, S. K., 2008. A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network. Journal of Intelligence and Information Systems. 14, 3, 25--39.
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@article{Shim:JIIS:2008:335,
author = {Shim, Hong Gi and Kim, Seung Kown},
title = {A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network},
journal = {Journal of Intelligence and Information Systems},
issue_date = {September 2008},
volume = {14},
number = {3},
month = Sep,
year = {2008},
issn = {2288-4866},
pages = {25--39},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Multilayer Perceptrons(MLP), General Neural Netwowk(GRNN), Wargame Simulation and Battalion Command in Battle Training },
}
%0 Journal Article
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%A Hong Gi Shim
%A Seung Kown Kim
%T A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 14
%N 3
%P 25-39
%D 2008
%R
%I Korea Intelligent Information System Society