A framework for key element evaluation of combat system

Wang, Peichao; Zhou, Yun; Wang, Jiang; Zhu, Cheng; Chen, Chao; Zhang, Weiming

doi:10.1109/smc.2017.8123103

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Currently, the principal component analysis (PCA) method is widely used in the construction of various evaluation indicator systems as a method for mathematical dimensionality reduction. An earlier study adopted the PCA method to establish a comprehensive decision-making model concerning the threat of the artillery target, which helps in quickly processing data and make effective decisions (Wang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Effectiveness for the Collaborative Combat of an Unmanned Aerial Vehicle Based on Grey Minimum Entropy

Sun

Zhang

Chang

et al. 2021

J. Aerosp. Technol. Manag.

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The main points to the evaluation of effectiveness for the collaborative combat of the unmanned aerial vehicle (UAV) lie within the construction of a reasonable indicator system and an accurate contribution model. As for point one, this article introduces a new method combining the Delphi consulting method and the principal component analysis method to avoid the underlying subjective and time-consuming defects of the existing methods. As for another point, a weighting method is adopted combining the subjective and objective parameters to minimize the errors caused by a single entity. Firstly, the modified grey relational degree analysis method is used to obtain the subjective weight, which can reduce the influence of the extreme values and outliers by enhancing the selection process of the reference sequence. Secondly, this paper adopts the weight of minimum entropy weight method to obtain the objective weight; it can avoid the information loss caused by the original method, which only determines the weight based on the frequency of each element present in the effective combination. At last, the principle of minimum relative entropy is adopted to obtain a more reasonable weight distribution coefficient. The simulation experiments established the rationality and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%