滑翔导弹末段多约束智能弹道规划

滑翔导弹末段飞行时空复杂度高、不确定性强、约束多,给弹道规划与制导算法带来了较大的建模和求解难度。针对这一问题,同时增大末段机动范围并提高弹道规划效率,本文提出一种利用连续型深度置信神经网络(ConvolutionalDeep Brief Networks,CDBN)预测机动能力、设计经由点状态实现末段多约束智能弹道规划的方法。过程中采用CDBN对机动能力进行在线预测,快速判定经由点状态的可行性,并且通过经由点状态智能设计,实现前后段能量的优化分配,扩大弹道机动包络;通过设计三角函数型弹目视线角实现末段弹道摆动机动,推导机动弹道最优末制导律对视线角进行跟踪,并调节机动频率以满足速度约束。仿真结果表明,CDBN相对BP网络具有更高的机动能力预测精度;本文所提智能弹道规划方法在满足末端速度约束的前提下,可以实现弹道摆动机动并大幅增加飞行包络。弹道规划能够在0.5s内完成,满足工程应用的快速性要求。

Prediction of Wing Aerodynamic Performance in Rain Using Neural Net

A new method for prediction of wing aerodynamic performance in rain condition was presented.Three-and four-layer artificial neural networks based on improved algorithm for error Back Propagation(BP)network were respectively built.Detailed approaches to determine the optical parameters for network model were introduced and the specific steps for applying BP network model to predict wing aerodynamic performance in rain were given.On this basis,the established optimal three-and four-layer BP network model was used for this prediction.Results indicate that both of the network models are appropriate for predicting wing aerodynamic performance in rain.The sum of square error level produced by two models is less than 0.2%,and the prediction accuracy by four-layer network model is higher than that of three-layer network.