IMM理论在超低空探测与跟踪上的应用

以超低空防御为背景 ,以防御系统雷达和红外传感器的融合为主线 ,运用概率互联理论和交互式多模型理论对超低空目标的探测和跟踪问题进行了仿真。经仿真认定 ,该理论不仅可用于目标的探测和预警 ,也可用于导弹的跟踪和制导     

基于模型预测控制的跳跃式再入制导算法研究

针对深空探测跳跃式再入返回飞行任务,提出了一种快速的再入制导算法,该算法基于模型预测控制（model predictive control,MPC）理论和近似动态规划（approximate dynamic programming,ADP）技术,将再入制导问题转化为两点边值问题,然后采用高斯伪谱法（Gauss pseudospectral method,GPM）求解该问题,实现快速制导计算。同时为了达到制导精度和制导效率的综合最优,提出了一种数值预报校正（numerical predictor-cor-rector,NPC）制导算法和快速再入制导算法融合的分段混合制导策略,该策略能对快速制导算法带来的制导偏差进行及时的修正,从而保证制导精度。蒙特卡罗仿真实验表明,与传统的数值预报校正制导算法相比,快速混合制导算法不仅能保证较高的制导精度,而且大幅减少了平均制导计算耗时,具有极大的在线应用潜力。     

基于PO的进近着陆系统信道环境分析和仿真预测

针对机场环境日益复杂,进近着陆系统对于机场环境的影响十分敏感的实际问题,提出了一种基于物理光学理论(PO)的进近着陆系统信道环境分析和仿真预测方法。应用基于PO的信道散射环境分析方法,建立了系统的信号源模型和考虑信道环境影响的系统航道偏移模型,对受多散射体影响的信道环境下的进近着陆系统进行建模,结合实际问题,应用建立的模型对散射环境中的进近着陆进行了仿真,结果表明,基于PO的仿真预测方法能够较准确、合理地给出散射环境对系统的影响程度,具有预测机场配置物体对系统影响的能力。     

基于快速网络模拟的安全态势预测

为提升网络安全态势预测的准确性,提出基于网络模拟的态势预测方法。该方法依靠网络模拟技术,详细推演网络安全行为的未来状态,并通过对状态数据进行分析与归纳,实现对网络安全态势的预测。为提升预测的时效性,研究核心节点路由静态存储以及动态连续计算等快速网络模拟技术。通过具有1720个节点的网络拓扑进行了实验验证:依靠快速网络模拟技术,整个态势预测所需的时间减少了51.7%,实现能提前获知未来的安全态势值;对所获得的安全态势曲线与实际安全行为进行对照分析,验证了安全态势预测的准确性。     

基于BP神经网络的增益量化中的阶距自适应

本文分析了G.728语音编码算法增益固定量化的缺陷,设计了精确表示增益的自适应量化方案。采用BP神经网络对增益的量化步长进行非线性自适应量化,并提出一种新的用于训练BP神经网络的数据的获取方法。     

基于HMM的多维数据下扶贫对象状态预测

针对扶贫领域中贫困、脱贫和返贫状态预测不准确,影响状态变迁的关键因素难以识别的问题,从扶贫基础数据和多个行业数据中提取8个关键特征和22个观测状态,构建观察状态和隐含状态关联关系,建立扶贫对象状态预测隐马尔可夫模型(hidden markov model,HMM)。以某深度贫困县连续3年的数据为样本,进行参数训练、测试实验和结果验证,结果表明该方法对返贫、贫困和脱贫状态有较强的预测能力,误差率较低,且能准确识别出影响返贫的关键要素。该方法对指导精准扶贫工作具有非常重要的实际意义。     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.