基于AR和ARMA模型的多变量非高斯风压模拟

基于多变量非高斯随机过程间的相关性,将发展的单变量非高斯过程自回归和自回归滑动平均(AR和ARMA)模型模拟算法扩展至多变量非高斯过程的数值模拟。通过AR和ARMA模型系数考虑多变量非高斯过程间的相关性,建立多变量非高斯过程AR和ARMA模型的模拟算法。多变量非高斯风压的数值模拟表明:AR和ARMA模型算法能有效地模拟低斜度、中斜度和高斜度的多变量非高斯随机过程。     

随机过程数字仿真的ARMA法

文章讨论了采用ARMA 模型进行随机过程数字仿真问题。研究表明,可由AR 模型导出具有高计算效率的ARMA 模型。模型阶数和采样间隔的选取应保证相关函数在主要相关区段上的拟合。ARMA 模型阶数只要高于一定值,都可达到较好的仿真效果。     

振动系统参数识别的ARMA模型与Ibrahim-Randec模型的关系

本文研究了用多变量ARMA模型描述振动系统的受迫响应进行参数识别的数学理论,给出随机减量函数的某些性质,指出了对受白噪声激励下的ARMA模型同Ibrahim-Randec之间的关系.     

海浪运动数字模拟的ARMA法

本文研究采用ARMA模型对海浪运动过程进行数字模拟的问题,引入了精确计算模型的输出—输入互相关函数的简单的迭代步骤,使ARMA模型参数的确定不依赖于高阶AR模型,从而避免通常的ARMA法中存在的计算困难,并使导出的模型能更可靠地实现P—M海浪谱。     

关于ARMA模型参数确定方法的改进

本文讨论了借助ARMA模型产生随机过程的问题,对通常的ARMA参数确定的2SLS步骤进行了修改,使模型的AR参数和MA参数分离确定。参数分离确定的步骤降低了模型输入-输出互相关函数估计误差对ARMA模型参数确定的影响,使得到的ARMA过程的谱更接近目标谱。     

高分辨率ARMA模型谱估计的一种快速算法

本文通过对C参数估计式的推导,论述了一种用ARMA模型的AR参数和自相关函数来估计C参数的方法,克服了在估计MA参数时常规迭代算法的复杂性,从而使得ARAM模型谱估计算法比较简便,仿真结果表明本文论述的算法具有计算量小,分辨率高的优点。     

双线性时间序列模型的参数辨识

导出了双线性时间序列模型参数预报误差估计的递推算法，采用伴随常微分方程的稳定性分析方法分析了双线性模型的ｍ－可逆性对可预报误差估计算法收敛性的影响，并给出了相应的仿真示例。     

线性动态系统噪声辨识的非参数贝叶斯推理算法研究

提出一新的非参数贝叶斯推理算法来辨识任意复杂的多模噪声分布,采用无穷维推理技术,能够较为精确地逼近噪声的后验分布.算法主要引入一随机度量分布满足一预设的先验过程--混合Dirichlet过程(Dirichlet Process Mixture,简称DPM),由于DPM具有形似于Polya urn的采样特性,能够很方便地对噪声数据进行聚类,并导出噪声的后验分布.仿真结果显示,噪声数据似然的Metropolis Hastings(M-H)的采样算法比点估计的系统分析算法精度高.     

基于参数估计的动态称重新方法

提出了一种新的动态称重法。首先导出了二阶称重系统的自回归滑动平均模型(ARMA);借助这一模型和递推最小二乘法(RLS),可以由极短的称重阶跃响应估计出模型参数和被称质量。数字仿真研究表明:在称重信号含有输入端白噪声污染的情况下,这一方法具有良好的准确度。     

一类离散时间代数Riccati矩阵方程异类约束解的双迭代算法

本文研究在最优控制系统中遇到的离散时间代数Riccati矩阵方程(DTARME)异类约束解的数值计算问题.首先对多变量DTARME中的逆矩阵采用矩阵级数方法进行等价转化,然后采用牛顿算法求多变量DTARME的异类约束解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程的异类约束解或者异类约束最小二乘解,建立求多变量DTARME的异类约束解的双迭代算法.双迭代算法仅要求多变量DTARME有异类约束解,不要求它的异类约束解唯一,也不对它的系数矩阵做附加限定.数值算例表明,双迭代算法是有效的.     

Simulation of earthquake records using time-varying Arma (2,1) model

In this paper, the time-varying auto regressive moving average (ARMA) process is used as a simple yet efficient method for simulating earthquake ground motions. This model is capable of reproducing the nonstationary amplitude as well as the frequency content of the earthquake ground accelerations. The moving time-window technique is used to estimate the time variation of the model parameters from the actual earthquake records. The method is applied to synthesize the near field earthquakes, Naghan 1977, Tabas 1978, and Manjil 1990 recorded on dense soils in Iran, as well as the Mexico City 1985 earthquake recorded on a site with soft soil. It is shown that the selected ARMA (2,1) model and the algorithm used for generating the accelerograms are able to preserve the features of the real earthquake records with different frequency content. The moving time-window technique is used to estimate the time-varying ARMA parameters. Using a general guideline, it is found that a reasonable estimate of the moving window and overlapping sizes can be obtained with a few trials. The required window and overlapping sizes generally reduce, as the local soil of the site becomes stiffer. The statistical response and Fourier spectra of the simulated accelerograms are compared with those of the actual records and reasonable agreement was found. The relationship between the ARMA (2,1) model and the continuous Kanai–Tajimi model is also studied. It is shown that the parameters of the nonstationary Kanai–Tajimi model for simulation of artificial earthquakes can be evaluated using the present modeling procedure.     

On ARMA models for vibrating systems

The paper considers the possible ARMA models which can be derived from the discrete-time state space model. This is achieved by the definition of the regular observation matrix. ARMA models of different order are obtained and the number of identified parameters of these models is determined. The almost sure existence of the minimal parameter ARMA model is shown. On this basis, the classification of ARMA models for vibrating systems is presented.     

An Approach for Modeling Small-lot Assembly Networks

A model for estimating the transient performance of assembly networks is presented in this paper. Based on the fundamental assumption that operation start and finish times are related by the multivariate normal distribution, the approach relies upon computational procedures for estimating the correlations between certain operation finishing times. Fundamental properties of these correlations are identified and used to develop a procedure for estimating transient performance. Evaluated in a set of hypothetical test cases, the approach gave estimates which compare favorably with those derived from a simulation model both in accuracy and runtime. The approach is demonstrated as a decision aid in a case study involving material flow planning. Test results indicate that the approach offers unique capability to model transient operations in assembly networks.     

一种水电厂房振动模态参数识别方法

水电厂房的振动是机械力、电磁力、水力脉动共同作用的结果,其动荷载很难测得,结构模态参数识别的难度不言自明。为解决以上困难,提高厂房结构振动模态参数识别的精度,在厂房结构各种荷载未知的情况下,将突然停机工况下动荷载释放后的振动信号,利用随机减量法提取自由衰减信号成分,以基于ARMA模型参数识别法实现了对某大型水电站厂房低阶模态参数的识别。识别结果表明,随机减量法和ARMA联合分析方法是解决大型复杂厂房结构动态参数识别的有效方法,识别结果可用于结构物的健康监测和振动控制中,同时该方法在大型水电站厂房振动模态参数的在线识别领域中具有广阔的工程应用前景。     

Wind field simulation for structural engineering purposes

This paper offers a survey of the issue of wind velocity modelling and simulation; the theme is studied considering stationary multivariate stochastic process, according to the prescribed cross-spectral density matrix. It also analyses extension to generation of vector fields and compares AR, ARMA and Shinozuka–Deodatis approaches in application to both small and real size examples. A number of ‘quality indices’ are chosen and used to assess the performance of the different numerical procedures. Wavelets are used together with standard Fourier transform in the process of checking the quality of the signals generated. Finally, the present study provides some guidelines concerning the optimal parameters to be chosen for the application of AR and ARMA procedures and a comment on the numerical efficiency of the different generation algorithms. Copyright © 2004 John Wiley & Sons, Ltd.     

A simulation method for stationary Gaussian random functions based on the sampling theorem

A unified method is developed for simulating realizations of real-valued stationary Gaussian processes, vector processes, fields, and vector fields. The method is based on parametric random models consisting of superpositions of deterministic functions of time or space with random amplitudes. The parametric models are based on the sampling theorem for random processes and generalizations of it for vector processes and random fields. The proposed simulation method is efficient and uses algorithms for generating realizations of random processes and fields that are similar to simulation techniques based on ARMA models. Several examples are presented to demonstrate the proposed simulation method and evaluate its efficiency and accuracy.     

Modelling and joint monitoring of input and output of systems with arbitrary order autoregressive disturbance

Considerable studies have been done on modelling and joint monitoring of input and output of systems with autoregressive moving average (ARMA) disturbance. Most of these studies focus on systems with ARMA (1, 1) disturbance. However, many kinds of systems are not conform to ARMA(1, 1) disturbance. Motivated by the fact that an autoregressive (AR) model with high order can be implemented to approximate the stationary ARMA model at any precision, a new generic model and a joint monitoring scheme of systems with arbitrary order AR(p) disturbance are developed. A minimum mean squared error (MMSE) controller with arbitrary order AR disturbance is designed to reduce the system variability. The mathematical expectation and average run length of MMSE-controlled outputs are derived. A new joint chart for monitoring the input and output simultaneously is explored. Two out-of-control rules for the joint monitoring chart are developed. The monitoring performances of the input chart, the output chart and the joint monitoring chart are also discussed. The results of simulation experiments and case studies validate the effectiveness of the developed model and joint monitoring chart.