Full velocity difference and acceleration model for a car-following theory

In order to describe the car-following behavior more actually in real traffic, a full velocity difference and acceleration model (for short, FVDAM) is proposed by synthetically taking into account headway, velocity difference and acceleration of the leading car on the basis of full velocity difference model. The analytical method and numerical simulation results show that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that incorporating the acceleration of the leading car into car-following model can stabilize traffic flow, suppress the traffic jam and increase capacity, and that the following car in FVDAM can accelerate more quickly than in FVDM.     

On the influence of design parameters and nonlinearities in vehicle suspensions on road deformation

Among others, two main objectives of modern vehicle design are road friendliness and ride comfort. Both aspects are strongly related since the dynamical tire forces depend on the vertical acceleration of the vehicle. In order to investigate the influence of design and operation parameters, different car models are considered which move with constant velocity on a rippled road. First, a linear half car model is examined and the influence of different design parameters is discussed. Second, nonlinear suspensions with Coulomb friction due to sealings as well as with bilinear shock absorbers are taken into account. The vertical dynamics of the vehicle model and the dynamic tire forces between vehicle and road are calculated using analytical methods. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Numerical bifurcation analysis of a ‘car‐following’ model

We study a system of ordinary differential equations describing a car‐following model for the motion of N car around a circular highway. All cars behave in the same way. The acceleration of each car is determined as a function of the headway (optimal velocity function). This model is known to have a solution with constant velocities and headways which, in a certain parameter regime, is stable and, varying the density of the cars, the loss of stability is generally due to a super‐ or subcritical Hopf bifurcation. Guided by analytical results, we numerically investigate the global bifurcation diagram for periodic solutions and obtain a complete picture of the dynamics of the model. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Sparse Vector Autoregressive Modeling

The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of the AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions, and difficult-to-interpret temporal dependence. To overcome such drawbacks, we propose a two-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero. The first stage selects nonzero AR coefficients based on an estimate of the partial spectral coherence (PSC) together with the use of BIC. The PSC is useful for quantifying the conditional relationship between marginal series in a multivariate process. A refinement second stage is then applied to further reduce the number of parameters. The performance of this two-stage approach is illustrated with simulation and real data examples. Supplementary materials for this article are available online.     

AR（p）模型中的缺失数据估计

运用EM算法,对含有缺失数据的AR（p）模型进行参数估计,通过最大似然准则就非左端缺失的情况进行插补.最后,用蒙特卡洛方法给出实验分析,表明如下结果：（i）误差与AR模型的阶数正相关,与缺失比例正相关;（ii）当AR模型的特征根模长相对较小时,误差与数据长度负相关,且误差被控制在了标准差的30％以内;（iii）当模长中等时,误差基本控制在1个标准差左右;（iv）当模长较大时,误差与数据长度正相关,而且误差也相对较大.     

A semiparametric method for estimating nonlinear autoregressive model with dependent errors

The first-order nonlinear autoregressive model is considered and a semiparametric method is proposed to estimate regression function. In the presented model, dependent errors are defined as first-order autoregressive AR(1). The conditional least squares method is used for parametric estimation and the nonparametric kernel approach is applied to estimate regression adjustment. In this case, some asymptotic behaviors and simulated results for the semiparametric method are presented. Furthermore, the method is applied for the financial data in Iran’s Tejarat-Bank.     

A car-following collision prevention control device based on the cascaded fuzzy inference system

A car-following collision prevention control device based on the cascaded fuzzy inference system (CFIS), consisting of a velocity fuzzy controller and an acceleration fuzzy controller, to nonlinearly control car acceleration or deceleration rate is proposed. The distance and speed relative to the car in front are measured using spread spectrum radar and applied to the collision prevention control device. The output acceleration or deceleration rate obtained from the CFIS car-following collision prevention system is based on the characteristics of the vehicle. The simulation results demonstrate that the presented CFIS control device can solve the oscillation problems for final relative distance between the lead vehicle (LV) and following vehicle (FV) and relative speed. When the LV applies the brake suddenly or a stationary obstacle appears in front of vehicle moving at high speed on the roadway, the CFIS control device can safely avoid a collision. The CFIS car-following collision prevention control device proposed in this paper can provide a safe, reasonable and comfortable drive.     

Testing coeffcients of AR and bilinear time series models by a graphical approach

AR and bilinear time series models are expressed as time series chain graphical models, based on which, it is shown that the coefficients of AR and bilinear models are the conditional correlation coefficients conditioned on the other components of the time series. Then a graphically based procedure is proposed to test the significance of the coeffcients of AR and bilinear time series. Simulations show that our procedure performs well both in sizes and powers.     

Vertical Vibrations of a Vehicle Excited by Real Road Profiles.

We analyze experimental acceleration data obtained from the delivery car vibration test in working conditions. The vertical components of unsprung and sprung mass accelerations have been analyzed by using the Fourier analysis. In particular we examine the response of the sprung mass on various road excitations and conclude the efficiency of the car suspension. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

误差非线性回归模型基于几何方法的若干二阶渐近性质

该文用微分几何方法对AR(q)误差非线性回归模型若干二 阶渐近性质进行了研究. 作者基于Fisher信息阵在欧氏空间定义了内积，并在期望参数空间建立了几何结构. 基于上述几何结构，给出了AR(q)误差非线性回归模型若干二阶渐近性质的曲率表示. 将前人的一些结果推广到AR(q)误差非线性回归模型.      

Testing coefficients of AR and bilinear time series models by a graphical approach

AR and bilinear time series models are expressed as time series chain graphical models, based on which, it is shown that the coefficients of AR and bilinear models are the conditional correlation coefficients conditioned on the other components of the time series. Then a graphically based procedure is proposed to test the significance of the coefficients of AR and bilinear time series. Simulations show that our procedure performs well both in sizes and powers. This work was supported by the Hong Kong Polytechnic University Research Council, the National Natural Science Foundation of China (Grant No. 10671044) and the Science and Technology Bureau of Guangzhou Municipal Government of China (Grant No. LSBH-017)     

CONFIDENCE REGIONS IN TERMS OF STATISTICAL CURVATURE FOR AR（q） NONLINEAR REGRESSION MODELS

This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results,, such as [1] and [2] are extended to AR(q)nonlinear regression models.     

New results in Sridhar filtering theory: The discrete case

A nonlinear time-varying adaptive filter is introduced, and its derivation using optimal control concepts is given in detail. The filter, which is called the discrete Pontryagin filter, is basically an extension to Sridhar filtering theory. The proposed approach can easily replace the conventional methods of autoregressive (AR) and autoregressive moving average (ARMA) models in their many applications. Instead of using a large number of time-invariant parameters to describe the signal or the time series, a single time-varying function is enough. This function is estimated using optimization techniques. Many features are gained using this approach, such as simpler and compact filter equations and better overall accuracy. The statistical properties of the filter are given, and it is shown that the signal estimate will converge in thepth mean to the true value.     

双重时序模型的高阶矩结构

为建立双重时序AR-MA模型的矩估计，除了需要模型平稳解序列的二阶矩结构[10]外，还需要建立平稳解序列的高阶矩结构[2]．设｛Xt｝为AR（1）－MA（q）模型的4阶平稳解序列．木文部分地证明了。｛X~2_t｝的相关结构为某一ARMA（3q，3q—1）型，这为．建立模型参数的矩估计创造了条件.     

具有随机效应和AR(1)误差的非线性纵向数据模型中组间方差和自相关系数的齐性检验

组间方差和自相关系数的齐性是纵向数据分析的基本假设之一,然而这种假设需要进行统计检验. Zhang \&; Weiss$^{[15]}$ 讨论了线性随机效应模型的组间和组内方差齐性的检验问题;林金官 \&; 韦博成$^{[10]}$ 研究了具有AR(1)误差但没有随机效应的非线性模型的自相关系数的齐性检验.该文研究具有随机效应和AR(1)误差的非线性模型的组间方差和自相关系数的齐性检验问题,构造了几个score检验统计量, 并通过Monte Carlo模拟方法研究了检验统计量的性质.最后利用该文的方法分析一组实际数据和一组模拟数据.     

Using ARMA models to forecast workpiece roundness error in a turning operation

The paper describes the methodology for developing autoregressive moving average (ARMA) models to represent the workpiece roundness error in the machine taper turning process. The method employs a two stage approach in the determination of the AR and MA parameters of the ARMA model. It first calculates the parameters of the equivalent autoregressive model of the process, and then derives the AR and MA parameters of the ARMA model. Akaike's Information Criterion (AIC) is used to find the appropriate orders m and n of the AR and MA polynomials respectively. Recursive algorithms are developed for the on-line implementation on a laboratory turning machine. Evaluation of the effectiveness of using ARMA models in error forecasting is made using three time series obtained from the experimental machine. Analysis shows that ARMA(3,2) with forgetting factor of 0.95 gives acceptable results for this lathe turning machine.     

A method for determining control inputs for prescribed accelerations of the end-effector of a motion-simulator robot

Described in this paper is an approach for generating control inputs for an industrial Kuka robot such that it produces prescribed linear acceleration time histories for a given point of the end-effector. The method takes into account the workspace limits, and uses the internal interpolation cycle of the robot controller to generate the corresponding trajectories that will produce the prescribed accelerations. The PI parameters of the internal interpolation are identified from test measurements. The control scheme consists in producing time-varying values for the desired angle values of the interpolation algorithm, making the changes as the algorithm proceeds and thus generating smooth trajectories. The resulting acceleration of these trajectories is compared to the prescribed accelerations, and an optimization procedure is run such as to determine the required time-histories of the desired angles for the interpolation algorithm such that both accelerations match. The approach is demonstrated for the example of a 500kg payload Kuka robot that is aimed at producing given motions as a physical motion simulator (e. g. for cars or complex working machines). It is shown that the desired accelerations can be generated accurately and with high repeatability, making the approach suitable for example for simulating car rollover tests. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

时间序列模型和神经网络模型在股票预测中的分析

利用MATLAB软件编程建立AR模型、RBF和GRNN神经网络模型,滚动预测上证指数开盘价、最高价、最低价和收盘价与实际价格对比,分析误差.结果表明,3种模型用于股票预测均是可行的,误差很小.AR模型不稳定,对个别预测较准;RBF和GRNN网络训练速度都很快,但GRNN比RBF预测效果好.     

The determinants of equity transmission between the new and used car markets: a hedonic analysis

Drawing on a data set containing 371082 observations on new and used cars from 2008, this study employs a hedonic model to estimate the determinants of prices in the primary and secondary car markets in Germany. We are specifically interested in identifying those vehicle attributes that are responsible for retaining the car’s value in the used car market. Beyond parameterizing the influence of technical features and brand name on the retail price, our model simultaneously generates a corresponding set of parameter estimates for the used car price, thereby allowing us to formally compare their magnitudes across the two markets. This comparison reveals that fuel consumption, in particular, is an important determinant of the price, one whose impact is higher in magnitude in the used car market than in the new car market. Large heterogeneity in how cars hold their investment value is also seen to depend on body type and brand/model name.     

航空结构声激励应力的仿真研究

利用回归分析方法,建立声激励下飞机襟翼振动总加速度和总应力对总声压的仿真模型,及总应力对振动总加速度的仿真模型.通过模式识别,得到结构应力响应与加速度线性和非线性关系的界限,以及结构应力响应、结构加速度响应与总声压线性和非线性关系的界限.