Name
Title | ||
|---|---|---|
A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry [and application to blood flow/pressure data] |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/10.951514 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
computational time,renal blood flow,renal autoregulation,blood pressure,impulse response functions,transfer functions,parameter estimation,gaussian white noise,autoregressive moving average processes,noise contaminated data,physiological models,transfer function relations,transient response,noiseless time series data,optimal search criterion,nonlinear arma model,a priori incorrect model-order selection,linear arma model,affine geometry,time series,computer simulations,identification algorithm,haemodynamics | Affine geometry,Autoregressive–moving-average model,Autoregressive model,Linear independence,Ramer–Douglas–Peucker algorithm,Linear system,Computer science,Algorithm,Estimation theory,Moving average | Journal |
Volume | Issue | ISSN |
48 | 10 | 0018-9294 |
Citations | PageRank | References |
8 | 1.14 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Lu | 1 | 8 | 2.49 |
| Ki Hwan Ju | 2 | 21 | 2.97 |
| Ki H Chon | 3 | 31 | 4.25 |