面向火炮射击密集度的随机因素稳健设计

为了实现对射击密集度的有效控制，解决考虑随机因素的射击密集度优化设计问题，利用基于随机模型和随机优化的稳健设计理论，提出一种指标要求导向型的随机因素参数区间计算方法。基于蒙特卡洛模拟，采用六自由度外弹道模型，利用某型大口径榴弹炮数据求解射击密集度并研究其统计特性。利用不合格概率、灵敏度指数两个稳健目标准则，构建射击密集度稳健设计随机模型。采用带精英策略非支配排序遗传算法并结合反向传播神经网络代理模型技术对稳健设计随机模型进行优化求解，确定符合指标要求的随机因素参数区间。对比分析结果表明，该方法可同时保证射击密集度指标的最优性和稳健性，对射击密集度的预测具有一定可行性。

Robust Design of Random Factors on Gun Firing Dispersion

To study the effective ways to improve the firing dispersion and solve its optimal design problem considering random factors, a calculation method of parameters interval oriented by the predefined indicators is proposed by using robust design theory based on random model and random optimization. A large caliber howitzer data is used to calculate firing dispersion based on Monte Carlo simulation and six degrees of freedom exterior ballistics model, and the statistical properties of firing dispersion are analyzed. A random model of robust design is built by calculating the objective criteria of robust design including unqualified probability and sensitivity index. Based on multi-objective genetic algorithm (NSGA-II) and BP neural network, the random model is optimized to determine the parameters interval of random factors which meet the requirements. The analysis results show that the proposed method can ensure the optimality and robustness of firing dispersion, and is feasible for the prediction of firing dispersion.