基于指标关联的舰载机出动架次率预测方法

doi:10.12305/j.issn.1001-506X.2023.11.17

Abstract

Abstract:

As a key indicator to measure the combat effectiveness of an aircraft carrier, the carrier aircraft's sortie rate is very important for the safe and efficient operation of the carrier-based aircraft system. Establishing a model that predicts the current sortie rate based on real-time data will provide an important reference for the aircraft carrier commander's real-time scheduling. Firstly, starting from the original data of indicators, based on big data correlation analysis, community discovery, and principal component analysis, the tree-like relationship between indicators is determined, so as to establish a sparse deep neural network. At the same time, in order to ensure better training effect, standardization, L2 regularization, and Adam optimizer are selected as the optimization algorithm of the neural network. The simulation results show that the proposed method can achieve fast, accurate and real-time prediction of the sortie rate of carrier aircraft under the mission of continuous dispatch.

Key words: carrier aircraft's sortie rate, sparse depth neural network, Adam optimizer, date standardization, regularization

CLC Number:

O231.5

Jianing DENG, Haixu LI, Qianglin AN, Enlai SHA, Ze WANG, Yu WU. Prediction method of carrier aircraft's sortie rate based on index correlation[J]. Systems Engineering and Electronics, 2023, 45(11): 3507-3515.

Figures/Tables 13

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指标	x₁	x₂	x₃	x₄	⋯
x₂	0.125 4	-	-	-	⋯
x₃	0.372 8	0.152 3	-	-	⋯
x₄	0.678 5	0.602 5	0.354 9	-	⋯
x₅	0.102 8	0.256 7	0.952 8	0.807 2	⋯
⋮	⋮	⋮	⋮	⋮	⋮

方法	最大误差/%	最小误差/%	隐藏层1节点总数	隐藏层2节点总数	计算时间/s
稀疏深度神经网络	0.660 7	0.286 5	60	4	0.677
非稀疏深度神经网络	1.167 1	0.371 9	60	4	0.447

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Prediction method of carrier aircraft's sortie rate based on index correlation

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