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Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model PurposeThe purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.Design/methodology/approachIn order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.FindingsIt is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.Originality/valueThe paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Grey Systems: Theory and Application Emerald Publishing

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2043-9377
DOI
10.1108/gs-09-2018-0043
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.Design/methodology/approachIn order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.FindingsIt is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.Originality/valueThe paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

Journal

Grey Systems: Theory and ApplicationEmerald Publishing

Published: Jan 1, 1

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