基于鲁棒自适应kalman滤波平台标定数据处理

摘 要：惯导系统作为飞行器导航系统的重要组成部分,其精度直接影响到飞行精度,为了更实现更精确的导航,通常采用射前自标定技术,从而补偿平台的误差。当飞行器竖立在发射场时,通常会受到各种干扰因素的影响,比如阵风,这些干扰会对射前标定的精度产生很大的影响。为了更好的补偿这部分干扰带来的误差,本文利用ARMA(2,1)建立平台漂移模型,考虑到系统噪声的方差和协方差等信息并不可知而且所建立的模型也会有误差,本文选用鲁棒自适应卡尔曼滤波对数据进行处理,结果表明,经过处理后的数据方差比原数据方差小很多,可以满足标定精度要求。     

An intelligent model selection and forecasting system

In this paper we present an intelligent decision‐support system based on neural network technology for model selection and forecasting. While most of the literature on the application of neural networks in forecasting addresses the use of neural network technology as an alternative forecasting tool, limited research has focused on its use for selection of forecasting methods based on time‐series characteristics. In this research, a neural network‐based decision support system is presented as a method for forecast model selection. The neural network approach provides a framework for directly incorporating time‐series characteristics into the model‐selection phase. Using a neural network, a forecasting group is initially selected for a given data set, based on a set of time‐series characteristics. Then, using an additional neural network, a specific forecasting method is selected from a pool of three candidate methods. The results of training and testing of the networks are presented along with conclusions. Copyright © 1999 John Wiley & Sons, Ltd.     

Univariate Forecasting Comparisons: The Case of the Spanish Automobile Industry

This paper investigates the forecasting ability of unobserved component models, when compared with the standard ARIMA univariate approach. A forecasting exercise is carried out with each method, using monthly time series of automobile sales in Spain. The accuracy of the different methods is assessed by comparing several measures of forecasting performance based on the out-of-sample predictions for various horizons, as well as different assumptions on the models’ parameters. Overall there seems little to choose between the methods in forecasting performance terms but the recursive unobserved component models provide greater flexibility for adaptive applications. © 1997 by John Wiley & Sons, Ltd.     

The usefulness of heuristic N(E)RLS algorithms for combining forecasts

There exists theoretical and empirical evidence on the efficiency and robustness of Non-negativity Restricted Least Squares combinations of forecasts. However, the computational complexity of the method hinders its widespread use in practice. We examine various optimizing and heuristic computational algorithms for estimating NRLS combination models and provide certain CPU-time reducing implementations. We empirically compare the combination weights identified by the alternative algorithms and their computational demands based on a total of more than 66,000 models estimated to combine the forecasts of 37 firm-specific accounting earnings series. The ex ante prediction accuracies of combined forecasts from the optimizing versus heuristic algorithms are compared. The effects of fit sample size, model specification, multicollinearity, correlations of forecast errors, and series and forecast variances on the relative accuracy of the optimizing versus heuristic algorithms are analysed. The results reveal that, in general, the computationally simple heuristic algorithms perform as well as the optimizing algorithms. No generalizable conclusions could be reached, however, about which algorithm should be used based on series and forecast characteristics. © 1997 John Wiley & Sons, Ltd.     

Structural Time-series Modelling of Monetary Aggregates: A Case Study for Eleven European Countries

Monetary aggregates for eleven European countries are analysed using the structural time-series methodology, paying special attention to unit root issues. Estimation of the parameters of the models is carried out by applying the asymptotic least squares (ALS) procedure. A comparison with the maximum likelihood estimates obtained via the Kalman filter shows that ALS is an alternative to Kalman filter estimation. The empirical results show that for only a small number of series the four variance parameters of the basic structural model are strictly positive. For the majority of the series the variance of the irregular component is equal to 0.©1997 John Wiley & Sons, Ltd.     

时间序列分析方法及人民币汇率预测的应用研究

在简要介绍时间序列模型的基础上,使用人民币／美元的日汇率值进行实证研究,建立相应的ARIMA模型和EGARCH模型并进行预测和评价。研究结果表明,EGARCH模型的预测结果较ARIMA模型理想,适合描述人民币／美元汇率的变动趋势。     

基于LabVIEW的作动筒位置信号采集

针对作动筒位置的传统检测方法所存在的缺点,基于虚拟仪器开发软件LabVIEW,实现了作动筒位置信号采集系统的设计,系统包括硬件和软件两部分:硬件部分由位移传感器、信号调理模块、数据采集卡和工控机组成;软件部分主要包括数据采集、数据处理与存储以及上位监控界面。该系统投运结果表明,系统运行可靠,稳定地实现实时采集,达到了规定的设计目标和其它要求。     

融合预训练的港口吞吐量LSTM预测模型

港口吞吐量时序变化数据量较小且变化快,传统LSTM神经网络在此类数据上易出现过拟合,导致模型预测性能不佳。针对此问题,本文提出融合预训练与LSTM时序模型,通过预训练捕获任务领域的全局信息,再用LSTM模型精确描述各个港口的吞吐量变化规律,以提升模型对全部港口吞吐量预测的准确性。以天津港等15个中大型港口过去二十一年的月吞吐量为实验数据,以BP、ARIMA、传统LSTM等预测模型和目前流行的GNN-LSTM模型为比较基准进行仿真实验,结果显示本文所提出的融合预训练的LSTM模型能有效解决LSTM神经网络的过拟合问题,整体预测准确率高于所有基准模型。与传统LSTM模型相比,基于预训练的LSTM的MAE指标平均降低45.2%%,最多降低80.0%。     

基于不足气象要素时间序列的小时界限值提取

气象要素质量控制中,往往是以全球或全国历史极值、气象站历史极值或季节性极值作为界限值检查标准,该方法在时间尺度上具有广泛性和粗糙性。为了使气象站气象要素界限值检查标准具有唯一性和精细性,将时间尺度缩至以小时为单位,利用全国1942个气象站2019年和2020年每小时气象数据,基于Python语言实现了一套确定每小时界限值的概率统计算法。由于时间尺度小,所以在相邻时刻,该界限值确定方法适用于具有规律性的气象要素（气温、气压和相对湿度）。经过样本统计,气温、气压和相对湿度的每小时界限值与其实际变化规律一致,证明了该算法的有效性,为后续质量控制界限值检查步骤的实施打下坚实基础。     

基于双向长短期记忆神经网络的配网电压异常数据检测

受自然环境、计量仪器等影响,量测数据会出现异常,导致调度人员错误决策,威胁电力系统安全稳定运行。为保障电力系统安全稳定运行,提出了一种基于双向长短期记忆(bidirectional long short-term memory, Bi-LSTM)神经网络的配网电压无监督异常数据检测方法。利用Bi-LSTM神经网络处理时序数据的双向特性,建立时序预测模型,通过对比预测值和实际值的误差检测异常数据。最后,基于某实际配网电压数据进行仿真验证,仿真结果表明:所提方法在准确率、F1分数等指标方面均优于决策树、K近邻、支持向量机、长短期记忆(long short-term memory, LSTM)神经网络。