Abstract
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.
Highlights
In recent time, with the quick development of artificial intelligence technology and electronic information technology in the military field, it is seen that the traditional mode of combat has gradually changed to become more intelligent and informational
In order to construct an accurate contribution model, this paper presents a joint effectiveness evaluation model combining the subjective and objective methods and uses the principle of minimum relative entropy to calculate a more reasonable weight distribution coefficient
Through the research on the effectiveness evaluation system of the unmanned aerial vehicle (UAV) collaborative combat, the following aspects are mainly completed: At first, the expert principal component analysis (PCA) method is proposed and is used to complete the construction of the indicator system, which significantly reduces the workload of the indicator selection
Summary
With the quick development of artificial intelligence technology and electronic information technology in the military field, it is seen that the traditional mode of combat has gradually changed to become more intelligent and informational. In order to construct an accurate contribution model, this paper presents a joint effectiveness evaluation model combining the subjective and objective methods and uses the principle of minimum relative entropy to calculate a more reasonable weight distribution coefficient. This paper constructs an evaluation model based on the modified gray correlation analysis method and uses the principle of minimum relative entropy to determine the elements present in the reference sequence. This method comprehensively considers the influence of each evaluation value of the indicator and greatly reduces the effect of the extreme values and outliers on the selected optimal data, thereby obtaining a more reasonable and subjective weight. For every indicator in the indicator layer, the weights obtained by the three weighting methods are sorted out, respectively
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