Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.     

基于数值模拟的轴对称矢量喷管性能预测数学模型

基于三维数值模拟，对不同轴对称矢量喷管在多种工作状态下的内外流场进行了研究，分析了扩张角及扩张段长度对喷管有效矢量角的影响.基于最小二乘曲面拟合理论，建立了自变量包括扩张角、扩张段长度/喉道直径、落压比/设计落压比、几何偏转矢量角的多变量轴对称矢量喷管性能预测数学模型，并根据已有实验数据，对该模型进行了验证结果表明:推力系数误差最大为0.41%，流量系数误差最大为1.58%，矢量角误差最大为1.76°.建立的数学模型通用性较强，实现了用统一的模型对不同喷管性能参数进行预测和分析，具有一定的工程意义.      

一种基于专家规则系统的仓储弹药调度优化方法

由于弹药调度过程中发生的突发情况，如吊车故障、人员受伤等问题的不确定性，传统数学优化方法难以完全解决。因此，提出了一种基于专家规则库的问询派发子任务方法，能很好地和数学优化算法相结合，解决了上述弹药调度过程中的不确定情况与突发情况，并且该算法具有便于计算机编程实现的优点。