Lu S, Ju K H, Chon K H
Department of Electrical Engineering and Center for Biomedical Engineering, City College of the City University of New York, NY 10031, USA.
IEEE Trans Biomed Eng. 2001 Oct;48(10):1116-24. doi: 10.1109/10.951514.
A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algorithm is developed for modeling time series data. The new algorithm is based on the concepts of affine geometry in which the salient feature of the algorithm is to remove the linearly dependent ARMA vectors from the pool of candidate ARMA vectors. For noiseless time series data with a priori incorrect model-order selection, computer simulations show that accurate linear and nonlinear ARMA model parameters can be obtained with the new algorithm. Many algorithms, including the fast orthogonal search (FOS) algorithm, are not able to obtain correct parameter estimates in every case, even with noiseless time series data, because their model-order search criteria are suboptimal. For data contaminated with noise, computer simulations show that the new algorithm performs better than the FOS algorithm for MA processes, and similarly to the FOS algorithm for ARMA processes. However, the computational time to obtain the parameter estimates with the new algorithm is faster than with FOS. Application of the new algorithm to experimentally obtained renal blood flow and pressure data show that the new algorithm is reliable in obtaining physiologically understandable transfer function relations between blood pressure and flow signals.
开发了一种线性和非线性自回归（AR）移动平均（MA）（ARMA）识别算法，用于对时间序列数据进行建模。新算法基于仿射几何概念，其显著特点是从候选ARMA向量池中去除线性相关的ARMA向量。对于先验模型阶次选择错误的无噪声时间序列数据，计算机模拟表明，使用新算法可以获得准确的线性和非线性ARMA模型参数。许多算法，包括快速正交搜索（FOS）算法，即使对于无噪声时间序列数据，也不能在每种情况下都获得正确的参数估计，因为它们的模型阶次搜索标准不是最优的。对于受噪声污染的数据，计算机模拟表明，新算法在MA过程中比FOS算法表现更好，在ARMA过程中与FOS算法表现相似。然而，使用新算法获得参数估计的计算时间比FOS算法更快。将新算法应用于实验获得的肾血流量和压力数据表明，新算法在获得血压和血流信号之间生理上可理解的传递函数关系方面是可靠的。
