دانلود مقاله ISI انگلیسی شماره 10012
عنوان فارسی مقاله

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 محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
The residual based interactive least squares algorithms and simulation studies
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Mathematics with Applications, , Volume 58, Issue 6, September 2009, Pages 1190-1197

کلمات کلیدی
شناسایی بازگشتی - برآورد پارامتر - حداقل مربعات - کنترل خود بخود حرکت میانگین -
پیش نمایش مقاله
پیش نمایش مقاله باقی مانده های تعاملی مبتنی بر حداقل مربعات الگوریتم ها و مطالعات شبیه سازی

چکیده انگلیسی

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.

مقدمه انگلیسی

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 [1]. Recently, Ding, Yang and Liu analyzed the consistency of the multivariable SG algorithm [2]; Ding and Chen presented a hierarchical SG algorithm for multivariable systems [3] and an auxiliary model based SG algorithm for dual-rate systems [4]; Ding et al. studied the performances of the SG algorithms for dual-rate systems based on the polynomial transformation technique [5] and [6]. 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 [7] and an extended stochastic gradient algorithm with a forgetting factor for Hammerstein nonlinear systems [8]; Ding and Wang discussed the gradient based identification algorithm for Hammerstein–Wiener ARMAX systems [9]. Finally, Zhang, Ding and Shi presented a multi-innovation SG parameter estimation based self-tuning control algorithm [10]. Because of the fast convergence rate of the least squares identification, it has received much attention in many areas, including signal processing [11], system identification and parameter estimation [12], [13], [14], [15], [16], [17], [18] and [19], adaptive control [5], [20] and [21]. For example, Ding and Chen presented a hierarchical LS algorithm for multivariable systems [22], whose consistency was studied in [23]; Ding and Chen proposed an auxiliary model based LS algorithm for dual-rate systems [24]; Ding, Liu and Shi studied the performances of the polynomial transform based LS algorithm for dual-rate systems [25].

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 [12] and [8].

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.