基于作战环境配置资源的出动架次率计算模型

彭雅欣; 黄 琦; 余明晖; 杨 杰; 苏厚胜

doi:10.11993/j.issn.2096-3920.2020.06.008

基于作战环境配置资源的出动架次率计算模型

doi: 10.11993/j.issn.2096-3920.2020.06.008

1.
华中科技大学人工智能与自动化学院, 湖北武汉, 430074
2.
中国舰船研究设计中心, 湖北武汉, 430064

基金项目: 国家自然科学基金资助(61403255); 国防基础科研资助项目资助(2017207B005).

详细信息

作者简介:
彭雅欣(1996-), 女, 在读硕士, 主要研究方向为航母作战、船舶设计、系统仿真与分析.

中图分类号: U674.771 TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2020-06-01
- 修回日期: 2020-07-26
- 刊出日期: 2020-12-31

Calculation Model of SGR Based on Operational Environment Configuration Resources

1.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
2.
China Ship Development and Design Center, Wuhan 430064, China

摘要

摘要: 舰载机、无人艇等海军武器的出动架次率(SGR)是判断其综合作战能力的关键指标, 作战环境的资源配置将影响SGR, 因此研究作战环境的配置资源最优组合将有利于提高系统的作战能力。文中以航母舰载机为例, 在对波次出动模型进行研究的基础上, 建立仿真SGR计算模型, 针对其需重复计算以降低计算结果随机性的问题, 利用反向传播(BP)神经网络对仿真模型进行拟合, 得到BP-SGR计算模型, 并使用该模型进行甲板资源配置优化研究。仿真实验表明, BP-SGR计算模型得到的21组最优资源配置组合SGR值, 均落在仿真模型SGR计算结果99%预测区间内, 且二者相对误差均小于1%, 验证了该模型在资源配置优化问题的适用性。使用该模型可有效对舰载机、无人艇等作战资源配置问题进行求解。
- 海军武器 /
- 出动架次率 /
- 资源配置 /
- 波次出动 /
- 反向传播神经网络
Abstract: The sortie generation rate(SGR) of naval weapons such as aircraft carriers and unmanned surface vessels (USVs) is the key index for judging the comprehensive operational capabilities of a system, where the resource configuration will greatly affect such a system. Therefore, research on the optimal combination of configuration resource of an operational system can improve the system’s operational capabilities. Using an aircraft carrier SGR as an example and based on the sortie-by-waves model, this study develops a simulation calculation model of the SGR. To avoid the problem of having to repeat the calculation to reduce the randomness of the results, a simulation calculation model is fitted by a back-propagation(BP) neural network, and a BP-SGR calculation model of is obtained. An optimization of the deck resource configuration with the BP-SGR calculation model is also studied. Through a simulation, all of the SGR combination values of 21 groups optimal resource configurations obtained by the BP-SGR calculation model fall within a 99% prediction range of the simulation model’s SGR calculation results. In addition, all relative errors are shown to be less than 1%, thus verifying the applicability of the model to resource configuration optimization. The BP-SGR calculation model can thus be used to solve configuration problems related to aircrafts and USVs.
- naval weapon /
- sortie generation rate(SGR) /
- resource configuration /
- sortie by waves /
- back propagation(BP) neural network

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