An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

TIME-VARYING AR MODELING AND ADAPTIVE IIR NOTCH FILTER FOR ANTI-JAMMING DSSS RECEIVER

Using Time-Varying AR (TVAR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in Direct Sequence Spread Spectrum (DSSS). The performance of TVAR model for Instantaneous Frequency (IF) estimation will be affected by some factors such as basis functions. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the Signal-to-Interference plus-Noise Ratio (SINR) of correlation output to the narrowband jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method.

基于自适应AR模型巡航飞行参数预测研究

为更准确实现飞行参数趋势预测,提出一种基于自适应自回归(AR)模型的稳定巡航飞行参数预测方法。根据稳定巡航参数筛选条件,获取建模所需飞行参数。利用卡尔曼滤波原理估计AR模型参数,并与飞行参数构建系统方程,利用无迹卡尔曼滤波实时更新、修正AR模型参数估计值,将自适应AR模型的预测值与曲线拟合模型和灰色模型的预测值进行对比。以波音B777-300ER飞机的快速存取记录器数据样本进行仿真验证,结果表明:自适应AR模型在数据预测和收敛速率方面均更优,可有效降低预报模型随步数增加导致的精度误差,提高参数预测准确性。研究在飞机维修保障、状态监控与预测等方面具有重要作用。

基于Lasso及Adaptive Lasso的AR(p)模型定阶及参数估计

Lasso类方法可以同时实现变量选择与参数估计,将之运用于AR(p)模型的定阶及参数估计,可以大大简化计算步骤和时间.本文在前人基础上利用Lasso类方法,改进了AR(p)模型的定阶与参数估计,通过计算机编程模拟,验证了此类方法的可行性,并比较了在不同样本量情况下,Lasso和Adaptive Lasso方法在定阶和参数估计两方面的优良性,最后将较优的Adaptive Lasso方法用于实际时间序列数据中,并对结果进行分析,指出了该方法的实用性.

风浪背景下舰船水压场信号的自适应AR模型预测滤波

为了有效地从风浪背景中检测舰船水压信号,首先根据舰船水压信号和波浪噪声的特点以时间序列的AR模型理论为依据,导出一种便于实现的自适应滤波器结构,并用NFTF自适应算法实现对舰船水压信号的预测滤波,取得了明显的效果.计算机仿真实验结果表明,给出的方法对抗波浪干扰具有良好的性能.

基于自适应AR模型的核电站松动件报警方法

为能快速准确地检测到核电站一回路零部件的松动或脱落,提出1种基于自适应AR(auto-regressive)模型的松动件报警方法。该方法利用自适应AR模型跟踪一回路中背景噪声的变化,先对信号进行白化处理,再计算白化后信号的短时均方根(RMS),设置RMS动态阈值实现报警。采用秦山核电站一号机组背景噪声和松动件碰撞信号叠加进行了仿真试验,结果表明,该方法能够在低信噪比和噪声复杂变化的条件下快速检测出松动件碰撞信号。

时变迭合AR模型的参数估计

首次提出了时变迭合ＡＲ模型，该模型在实际应用中具有广泛的应用价值．应用两步最小二乘法和限定记忆递推最小二乘法，给出了模型中时变参数的递推估计算法，该算法仅依靠量测数据即能自适应进行．仿真计算及应用结果表明：算法能够自适应地跟踪量测数据模型参数的变化，效果是令人满意的．

两种植物电信号的自适应AR参数模型分析

根据植物电信号非平稳、非线性等特点,以盆栽植物君子兰和观音莲为研究材料,对采集的两种植物电信号进行自回归模型(AR模型)分析,并采用自适应技术对模型参数进行优化。首先采用Akaike信息检验准则估计植物电信号的模型阶次,然后基于最小二乘参数估计法建立植物电信号模型并拟合,最后利用自适应AR参数算法对两种植物电信号进行预测。结果表明:基于最小二乘法的AR模型对植物电信号进行短期预测是可行的。为了解决预测中误差随预测点数增加而增大的问题,提出了基于Kalman滤波算法的AR模型参数估计法,使得预报精度以及收敛速度得以优化。进一步预报的数据可作为温室或塑料大棚控制系统中重要的输入参数,该方法为植物生物信息学提供了新的研究基础。

基于自适应滤波和AR模型的ARM检测技术

根据反辐射导弹(ARM)的加速度明显的特征,可以应用AR模型来检测ARM。但在载机信号与ARM信号功率比过高时,也不能将ARM信号有效的检测出来。针对这种情况,提出一种基于自适应线性滤波和AR模型的反辐射导弹检测技术,首先利用自适应线性预测滤波法对雷达回波在时域进行载机信号抑制,然后用AR模型进行功率谱估计。仿真结果表明,该方法能够将微弱的ARM信号准确的检测出来,从而实现实时告警。

基于AR模型逼近的AMTI滤波器设计方法

提出了一种新的雷达自适应动目标显示(AMTI)设计方法。该方法利用AR模型逼近杂波谱“倒置”理想带通滤波器的频率响应,实现了最优FIR滤波器的设计。由于被逼近的“倒置”理想带通滤波器的中心频率和通带宽度可随杂波功率谱的变化适时调整,使AMTI滤波器的杂波抑制性能与经典自适应方法相比得到有效提高。雷达实际采集数据的对比实验结果表明该方法设计出的AMTI滤波器的优越性和实用性。

Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis

The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.

基于AR模型的二维自适应提升小波变换算法

研究先更新再预测的经典自适应提升小波算法,提出一种基于自回归(AR)模型的二维自适应提升小波变换算法。根据图像局部特性选择自适应更新算子,利用更新后的系数位置关系给出基于AR模型的预测算子,使预测误差功率最小。实验结果表明,与使用最小均方误差标准的自适应预测算法相比,该算法能够降低高频系数能量,且峰值信噪比也有所提高。

自适应AR模型及在形变监测中的应用研究

在国家一带一路倡议与实施战略的引导下,随着我国基础建设的技术水平快速提高,基础设施的建设速度快速提高,规模迅速扩大,把握与维持这些重大设施与工程的安全成为一项紧迫而又重要的任务。形变监测是这项任务的典型技术手段。本文针对形变模拟分析中常用的自回归模型(AR)的应用现状与存在问题,提出了基于相关性深化分析的自适应AR模型阶数控制方法,通过模拟结果的残差相关分析,补偿改进AR模型的有效阶数,使之更客观地反映变形序列的变化规律,扩展了序列分析与形变模拟的有效性,并且以某高楼的形变研究为例,通过实际观测数据和应用实例验证了改进模型的可行性。

传感器自适应AR模型的建模方法

本文分别采用ＬＭＳ和ＲＬＳ算法建立腕力传感器和压力传感器的自适应ＡＲ模型，计算结果与实测值吻合，并对各方法的特点进行了讨论．

A PREDICTION OF UT SERIES WITH AUTOADAPTIVE AUTO-REGRESSIVE MODEL

The prediction of UT can be separated into two parts, i.e. the prediction of a definitivecomponent and that of a random component. In this paper, the first part is carried out withlinear fitting extrapolation and periodic fitting extrapolation of NEOS UT1-UTC series ofone-day interval with a span of two years, and the second part with an RLS recursive proce-dure of auto-adaptive AR modeling. The combination of the two predicted values gives asatisfying result that the prediction precision reaches 0″.0038 with a lead time of 60 days.

网络化控制系统时延测量、分析与预测

针对网络化控制系统(NCS)中的随机时变时延,采用自行开发的基于应用层的测试软件,按照IETF(The Internet Engi-neering Task Force)的RFC2544规范,进行了6个多月实际测量,获得了170余万个网络时延数据;通过对实验数据进行相关分析,建立了网络时延的自适应自回归(AR)模型;并用自适应最小均方差(LMS)算法对实测的网络时延进行了估计和预测,实验结果表明了算法的有效性。

一种稳健的共形阵机载雷达杂波抑制方法

针对共形阵机载雷达的杂波抑制问题,提出了一种稳健的空时自适应处理(STAP)方法.该方法首先将待检测距离单元中所含的目标信息剔除,然后利用自回归(AR)模型来描述该单元杂波特性,最后通过求取AR模型系数得到待检测距离单元中杂波正交子空间来对消杂波.该方法不受雷达工作模式影响,适用于任意形状的阵列天线,且不存在干扰目标问题,因此较已有的多普勒补偿类、直接数据域和参数化等STAP方法更为稳健.仿真结果验证了该方法的有效性.

多普勒测速声呐的自适应Kalman滤波算法

针对小型自主水下无人潜器的运动特点,设计了一种加速度信息辅助的自适应Kalman滤波器,用于多普勒测速声呐的数据处理.首先基于时间序列分析法,建立了多普勒测速声呐噪声信号的AR模型,并根据当前统计模型设计了多普勒测速声呐Kalman滤波方程.然后针对滤波方程特点,设计了基于S面的自适应Kalman滤波器.实验结果表明,在已知加速度先验信息的条件下,基于S面的自适应Kalman滤波器能够根据小型水下机器人的运动特点实时调整滤波参数,滤波精度优于0.04m/s,且能够有效消除时间延迟,为小型水下机器人控制系统提供准确及时的速度信息.

基于邻域自适应局部保持投影的轴承故障诊断模型

为了有效利用振动信号进行故障诊断,提出了一种基于邻域自适应局部保持投影的轴承故障诊断模型。首先,利用EMD将轴承振动信号分解为若干个平稳的固有模态函数(IMF),对IMF分量建立自回归(AR)模型,构建原始特征子集。然后,利用邻域自适应局部保持投影算法对原始特征子集进行降维处理,获得原始特征子集的低维特征向量和投影矩阵。以低维特征向量为输入,以最小二乘支持向量机(LS-SVM)为分类器,通过研究故障识别率和低维特征空间维数的关系确定最优降维维数和对应的最优投影矩阵。最后,根据最优降维维数完成降维处理过程,得到低维特征向量,输入LS-SVM分类器,识别轴承的工作状态和故障类型。实验结果表明,该模型提高了轴承故障诊断的精度。