باقی مانده های تعاملی مبتنی بر حداقل مربعات الگوریتم ها و مطالعات شبیه سازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|10012||2009||8 صفحه PDF||سفارش دهید||3870 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Mathematics with Applications, , Volume 58, Issue 6, September 2009, Pages 1190-1197
This paper presents a two-stage least squares based iterative algorithm, a residual based interactive least squares algorithm and a residual based recursive least squares algorithm for identifying controlled autoregressive moving average (C-ARMA) models. The simulation studies indicate that the proposed algorithms can effectively estimate the parameters of the C-ARMA models.
The time series contains three basic models: autoregressive (AR) model, moving average (MA) model and autoregressive moving average (ARMA) model. This paper considers the least squares identification problem of controlled autoregressive moving average (C-ARMA) model. Compared with least squares (LS) algorithm, the stochastic gradient (SG) algorithm has small computational load but slow convergence rate . Recently, Ding, Yang and Liu analyzed the consistency of the multivariable SG algorithm ; Ding and Chen presented a hierarchical SG algorithm for multivariable systems  and an auxiliary model based SG algorithm for dual-rate systems ; Ding et al. studied the performances of the SG algorithms for dual-rate systems based on the polynomial transformation technique  and . In order to improve the convergence rate of the SG algorithm, Ding and Chen developed a multi-innovation SG identification algorithm for linear regression model  and an extended stochastic gradient algorithm with a forgetting factor for Hammerstein nonlinear systems ; Ding and Wang discussed the gradient based identification algorithm for Hammerstein–Wiener ARMAX systems . Finally, Zhang, Ding and Shi presented a multi-innovation SG parameter estimation based self-tuning control algorithm . Because of the fast convergence rate of the least squares identification, it has received much attention in many areas, including signal processing , system identification and parameter estimation , , , , , ,  and , adaptive control ,  and . For example, Ding and Chen presented a hierarchical LS algorithm for multivariable systems , whose consistency was studied in ; Ding and Chen proposed an auxiliary model based LS algorithm for dual-rate systems ; Ding, Liu and Shi studied the performances of the polynomial transform based LS algorithm for dual-rate systems .
نتیجه گیری انگلیسی
The paper presents a residual based least squares algorithm and a residual based interactive least squares algorithm for C-ARMA models. The methods in this paper can be extended to finite impulse response Hammerstein nonlinear systems with autoregressive moving average noise  and .