Intelligent Effectiveness Evaluation and Optimization on Weapon System of Systems Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.20-0353

Abstract

Abstract: In the process of conventional WSoS (Weapon System of Systems) operational effectiveness evaluation, multiple complex steps including reconfiguring the scenario, rerunning the WSoS simulation system, and conducting effectiveness evaluation model calculations are taken. For the problems of conventional effectiveness evaluation method, such as the complex process and time-consuming, the intelligent evaluation method of WSoS combat effectiveness based on machine learning regression is proposed, and the optimization process of combat effectiveness based on evolutionary strategy is introduced. Taking the full connection depth regression network as the prediction model, the guiding selection principles of network hidden layer number and sample size are given on the basis of the test results of multiple data sets. The optimal output is obtained by the genetic algorithm to adjust the network input, and the high iterative performance is got. Case study of a simulation system preliminarily verifies the effectiveness and value of the prediction model and the optimization method.

Key words: weapon system of systems, intelligent evaluation of operational effectiveness, intelligent optimization of operational effectiveness, regression prediction, deep regression network

CLC Number:

TP391.9

Li Ni, Li Yuhong, Gong Guanghong, Huang Xiaodong. Intelligent Effectiveness Evaluation and Optimization on Weapon System of Systems Based on Deep Learning[J]. Journal of System Simulation, 2020, 32(8): 1425-1435.

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