一种地震动信号特征提取与分类方法

为了实时检测、识别和预警对地下基础设施的挖掘破坏活动，本文提出一种地震动信号特征提取与分类方法。通过提取小波包变换域和集合经验模态变换域的多域能量联合分布特征向量，构建改进的径向基神经网络分类模型，利用机器学习的方法提取稳定的信号多域融合特征，并实现准确的信号特征分类预测。由多类别挖掘信号的仿真实验结果可以看出，本文的算法和模型能有效提升地震动信号分类的准确率，对地震动干扰信号具有较强的鲁棒性。     

Ф300mm大口径平面等厚干涉仪波面数字化技术改造

介绍了把Ф300 mm口径平面等厚干涉仪改造为移相式数字平面干涉仪的基本情况。改造后的仪器采用液面基准的系统误差校准方法,在Ф300 mm口径时的最大允许误差为76 nm（PV值）,测量重复性为4 nm,满足相关使用要求。     

An efficient large-scale mesh deformation method based on MPI/OpenMP hybrid parallel radial basis function interpolation

An efficient MPI/OpenMP hybrid parallel Radial Basis Function (RBF) strategy for both continuous and discontinuous large-scale mesh deformation is proposed to reduce the computational cost and memory consumption. Unlike the conventional parallel methods in which all processors use the same surface displacement and implement the same operation, the present method employs different surface points sets and influence radius for each volume point movement, accompanied with efficient geometry searching strategy. The deformed surface points, also called Control Points (CPs), are stored in each processor. The displacement of spatial points is interpolated by using only 20–50 nearest control points, and the local influence radius is set to 5–20 times the maximum displacement of control points. To shorten the searching time for the nearest control point clouds, an Alternating Digital Tree (ADT) algorithm for 3D complex geometry is designed based on an iterative bisection technique. Besides, an MPI/OpenMP hybrid parallel approach is developed to reduce the memory cost in each High-Performance Computing (HPC) node for large-scale applications. Three 3D cases, including the ONERA-M6 wing and a commercial transport airplane standard model with up to 2.5 billion hybrid elements, are used to test the present mesh deformation method. The robustness and high parallel efficiency are demonstrated by a wing deflection case with a maximum bending angle of 45° and more than 80% parallel efficiency with 1024 MPI processors. In addition, the availability for both continuous and discontinuous surface deformation is verified by interpolating the projecting displacement with opposite directions surface points to the spatial points.     

运载火箭推力故障下基于智能决策的在线轨迹重规划方法

为了提高运载火箭上升段飞行中推力故障下轨迹重规划的计算效率,提出了一种基于智能决策的在线轨迹重规划方法,将原问题转化为最优救援轨道的在线智能决策和成熟的燃料最优轨迹规划问题进行求解。通过离线求解不同故障状态对应的轨迹重规划问题,建立"故障状态-救援轨道"样本集,基于径向基神经网络建立最优救援轨道的决策模型,并将决策模型装订箭上。在实际飞行中以当前故障状态作为输入,利用决策模型可在线快速决策出最优救援轨道根数,进而求解以救援轨道为目标轨道的燃料最优轨迹规划问题,从而完成推力故障下的在线轨迹重规划。数值仿真表明,与直接求解轨迹重规划问题相比,该方法的计算效率提高了两个数量级以上,同时给出一致的最优救援轨迹。     

归一化RBF网络的结构优化策略

针对归一化RBF网络，利用基于山峰函数的减法聚类算法构造归一化RBF网络的初始结构，采用奇异值分解(SVD)算法分析了网络隐含层节点与奇异值、累积贡献率以及索引向量的关系，并对归一化RBF网络的结构进行了优化。最后，对该优化策略的可行性和有效性进行了仿真验证和性能比较。     

Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models from limited input–output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. In this study, the process of the Dst index is treated to be a structure-unknown system, where the solar wind parameter (VBs) and the solar wind dynamic pressure (P) are the system inputs, and the Dst index is the system output. A novel multiscale RBF (MSRBF) network is introduced to represent such a two-input and single-output system, where the Dst index is related to the solar wind parameter and the dynamic pressure, via a hybrid network model consisting of two submodels: a linear part that reflects the linear relationship between the output and the inputs, and a nonlinear part that captures the effect of the interacting contribution of past observations of the inputs and the output, on the current output. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using a forward orthogonal regression (FOR) algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems.     

基于近似模型的卫星动力学多目标优化

对基于近似模型的卫星结构动力学多目标优化进行了研究。给出了响应面模型、Kriging模型和径向基函数（RBF）模型三种近似模型的原理。基于某卫星结构有限元模型建立了优化的三种近似模型,给出了优化流程。分析结果表明：RBF模型的精度最高,该模型结合遗传算法的优化方法不仅显著降低了计算时间成本,而且有良好的精度。     

漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

 讨论了漂浮基空间机器人在轨捕获目标卫星过程的碰撞动力学建模,以及捕获操作结束后空间机器人与卫星混合体的稳定控制问题。首先采用多刚体动力学建模方法并结合空间机器人捕获目标卫星过程中的碰撞动力学特性,建立了漂浮基空间机器人在轨捕获漂浮卫星过程的动力学模型,并在此基础上计算出完成捕获操作后空间机器人与目标卫星混合体关节的运动速度。然后针对卫星及空间机器人系统惯性参数均是未知的复杂情况,应用上述模型、神经网络控制理论和Lyapunov稳定性理论,设计了空间机器人与卫星混合体在捕获过程碰撞冲击影响下稳定运动的高斯径向基函数神经网络控制方案,以达到对捕获卫星的有效控制。此外,高斯径向基函数神经网络控制方案具有不需要测量和反馈载体位置、移动速度与加速度的显著优点。系统数值仿真证实了上述控制方案的有效性。     

基于径向基函数的网格变形及非线性气动弹性时域仿真研究

为开展非线性气动弹性研究,基于非线性结构有限元软件NASTRAN和自主研制的多块结构化计算流体力学(CFD)求解器,开发了一套基于计算流体力学/计算结构动力学(CFD/CSD)耦合求解方法的气动弹性时域仿真程序.该程序采用径向基函数(RBF)交换两套求解器之间的数据并进行网格变形.为提高RBF方法的效率,构造了基于多次插值的空间待插值点精简算法.在多次插值过程中,每次插值的对象为上次插值的误差,并同时限制插值区域,以此实现了空间待插值网格数的精简.数个网格变形的算例表明该方法可支持大变形运动,并且具有较高的计算效率.采用此程序开展了AGARD 445.6机翼颤振计算、大展弦比机翼的静气动弹性计算与切尖三角翼极限环振荡(LCO)现象的动气动弹性仿真,结果揭示了当机翼展弦比较大或者响应幅值较大时,结构非线性对于气动弹性有显著影响.     

基于响应面插值的非线性气动弹性计算

飞翼、大展弦比低雷诺数气动布局容易在小迎角的条件下出现气流分离,会带来明显的非线性气动力问题,同时气动弹性带来的影响亦不可忽略。针对此类布局提出一种建立基于径向基函数插值的非线性压力系数分布模型的方法,利用径向基函数插值建立面元上压力系数对迎角导数的响应面,将压力系数积分并通过无限板样条(IPS)方法进行气动结构多次迭代插值实现非线性气弹分析。结果验证了该方法对于静气动弹性分析的有效性,同时能准确地反映弹性带来气动效率的降低和变形对升力及阻力的影响。