基于极限学习机的武器装备作战效能全局敏感性分析

Global Sensitivity Analysis and Optimization of Submarine Combat Effectiveness Based on Extreme Learning Machine

作战效能是衡量武器有效性的关键指标。通过寻找影响作战效能的敏感性指标来提高武器装备的作战效能是一种简单有效的方法。为解决复杂评估模型计算成本高、计算时间缓慢的问题,本文引入极限学习机作为代理模型,替代复杂的效能评估模型。运用基于方差的全局敏感性分析,找到影响武器作战效能的敏感指标,进而找到与其关联的武器设备,对其功能进行完善和提高,从而提高武器的作战效能。本文以潜艇典型作战任务为作战效能敏感性分析的案例,分别与基于前馈神经网络模型、支持向量回归模型为代理模型的全局敏感性分析进行对比,验证该模型的有效性和高效性。

Operational effectiveness is a key indicator to measure the effectiveness of weapons. It is a simple and effective way to improve the operational effectiveness of weapons by finding operational effectiveness sensitive indicators. In order to solve the problems of high computation cost and low computation velocity of complex evaluation model,this paper introduces the extreme learning machine as the agent model to replace the complex performance evaluation model. To improve the operational effectiveness of weapons,this paper uses variance-based global sensitivity analysis to find the key factors that affect the effectiveness of weapons,and then find the equipment associated with its function to be improved. This paper uses the typical combat mission of the submarine as the effectiveness optimization case to evaluate the feasibility of the method. Compared with the global sensitivity analysis based on the feed-forward neural network agent model and the support vector regression model,the experimental results verify the validity and efficiency of L-EML model.