Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

PLC上位机监控软件的开发

为提高硫化机控制水平，采用电阻式触摸平板电脑为上位机与PLC相结合。系统设计主要包括各监控画面的设计和上位机与PLC之间的通信设计，监控主画面设计成包含所有操作员需要了解的数据。实时曲线画面为圆盘型和直线型两种，上位机与PLC之间的通信采用VB6.0编程，实际运行证明状态良好。     

基于分层动态时间弯曲的序列相似性度量方法研究

为了更好地体现时间序列的形态特征,并探索更适合于较长时间序列之间相似性度量的方法,在动态时间弯曲算法的基础上进行改进,提出了基于分层动态时间弯曲的序列相似性度量方法。对时间序列进行多层次分段,并从分段中均匀抽取相对应的层次分段子序列,然后将层次分段子序列抽象为三维空间的点（反映了分段子序列的均值、长度和趋势）进行相似性度量,最后综合各个层次的相似性度量作为结果。实验表明,在参数设置合理的情况下,此方法能获得较高的序列相似性度量准确度和效率。     

宽带功率放大器PGSC建模方法及预失真研究

针对宽带功率放大器的强记忆效应特性,提出一种功放建模和数字预失真方法——PGSC模型。利用广义记忆多项式(GMP)、特定交叉项(SCT)及记忆时刻信号交叉项(CIMT)3个基函数来构造功放行为模型及数字预失真器,并搭建实际测试平台对模型的精度及线性化效果进行验证。测试结果表明,与PMEC方法相比,PGSC方法建模时的归一化均方误差减少了2.1 dB,数字预失真时输出信号的三阶邻信道功率比降低了4.94/2.03 dB;与GMP方法相比,PGSC方法仅利用73%的系数即可得到更高的模型精度和更好的线性化效果。     

FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

一种关于模糊时间序列模型论域划分的多尺度比率算法

针对模糊时间序列研究中比率划分论域方法存在对非均匀数据划分效果不理想的缺陷, 提出一种基于模糊C 均值聚类(FCM) 算法的多尺度比率划分论域的方法. 首先利用FCM算法对样本数据进行分类; 然后计算各类数据的平均相对误差, 并基于各类的平均误差划分论域, 产生非等间隔的多尺度论域划分方法; 最后, 通过算例表明了多尺度比率论域划分方法的有效性.     

REVERSED RESIDUALS IN AUTOREGRESSIVE TIME SERIES ANALYSIS

Abstract. Both linear and non-linear time series can have directional features which can be used to enhance the modelling and investigation of linear or non-linear autoregressive statistical models. For this purpose, reversed p th-order residuals are introduced. Cross-correlations of residuals and squared reversed residuals allow extensions of current model identification ideas. Quadratic types of partial autocorrelation functions are introduced to assess dependence associated with non-linear models which nevertheless have linear autoregressive correlation structures. The use of these residuals and their cross-correlation functions is exemplified empirically on some deseasonalized river flow data for which a first-order autoregressive model is a satisfactory second-order fit. Parallel theoretical computations are undertaken for the non-linear first-order random coefficient autoregressive model and comparisons are made. While the data are shown to be strongly non-linear, their correlational signatures are found to be convincingly different from those of a first-order autoregressive model with random coefficients.     

基于改进符号化度量方法的机场噪声异常检测

机场噪声中的异常情况拥有很大价值,利用它能够及时完善飞机和机场的设备。结合机场噪声数据的特点,对上述问题进行研究并提出一种基于改进的符号化聚集近似(Symbolic Aggregate Approximation,SAX)相似性度量的单监测点的时间序列异常检测方法。其运用相似性度量方法计算出度量结果,再运用k近邻异常检测方法进行异常发现,最后发现异常时间段。该方法在理论验证可行性之后在某机场的实测数据中进行应用,取得了良好的效果。     

MULTIVARIATE WALSH-FOURIER ANALYSIS

Abstract. In this paper we establish a statistical methodology for the spectral analysis of stationary multivariate time series via the Walsh-Fourier transform. Theoretical results pertaining to the definition and estimation of the Walsh-Fourier spectral matrix and functions of that matrix including cross-spectra, coherency and phase are given. An example of the statistical techniques developed in this paper is given; in particular, the methodologies are applied to neonatal sleep data collected from a study of the effect of maternal substance use during pregnancy.     

考虑土体强度参数深度依赖性的非平稳随机场边坡滑动深度和体积分析

基于随机有限元方法（RFEM）和Monte-Carlo模拟,建立了能够模拟具有深度依赖性的土体强度参数边坡或具有软弱夹层边坡的非平稳随机场模型。通过辐射扫描方法获取最危险滑动面,得到非平稳随机场边坡滑动深度和滑动体积。将该方法与均质边坡和平稳随机场边坡获得的滑动深度和滑动体积进行对比,研究土体强度参数深度依赖性的非平稳随机场边坡的不同坡度和各向异性对滑动深度和滑动体积的影响。结果表明：基于RFEM的非平稳随机场滑动深度和滑动体积结果为地质灾害风险及后果评估提供了一个参考分析的新角度;参数的深度依赖性程度越高(变化率b越大),则边坡平均滑动深度和滑动体积越小;在大坡度滑坡风险后果评估中,可以基于均质边坡分析结果进行初步预估;在非平稳随机场中,随着各向异性的增大,所有坡度边坡的滑动体积变异系数增大;不同坡度下,水平方向的相关程度越大,则土坡内软弱夹层的不确定性越强,滑动体积的变异系数也不断增大。