The model parameters are estimated using a recursive extended least square method 用遞歸擴展最小二乘法估算了模型參數。
To identify the model parameters of controlled esvrst system on line , the recursive extended least square with variety forgetting factor is used , which makes the control system possess anti - disturb ability obviously and unchangeable precision of stochastic waveform reappearance 本文提出了用變遺忘因子的增廣最小二乘遞推算法在線識別電液伺服汽車道路模擬試驗臺被控系統(tǒng)的模型參數,使控制系統(tǒng)在實現波形再現時具有顯著的抗干擾能力和穩(wěn)定的波形再現精度。
Compared with classical recursive extended least squares , their accuracy obviously is improved . applying these new algorithms to the parameter estimation problem for systems with measurement noises , some new approaches and algorithms of parameter estimation for system with measurement noises , are presented , for example , two - stage rels - gevers - wouters algorithm and three - stage rls - pi - gevers - wouters algorithm , which solve the biased parameter estimation problem by classical least squares method 并將這些算法推廣到帶觀測噪聲系統(tǒng)參數估計的問題,給出了帶觀測噪聲系統(tǒng)參數估計的一些新方法和新算法,其中包括兩段rels - gevers - wouters算法和三段rls - pi - gevers - wouters算法,解決了用普通最小二乘法估計帶觀測噪聲系統(tǒng)未知參數的有偏問題。
Based on the classical least squares method ( rls ) in system identification , the several new identification algorithms of parameter estimation for the autoregressive moving average ( arma ) model , are presented . they include univariable and multivariable two - stage recursive least squares - recursive extended least squares ( rls - rels ) and two - stage recursive least squares - pseudo - inverse ( rls - pi ) algorithms 本文在系統(tǒng)辨識經典的最小二乘法( rls )的基礎上,提出了自回歸滑動平均( arma )模型參數估計的一些新的辨識算法,它包括單變量和多變量兩段遞推最小二乘?遞推增廣最小二乘( rls ? rels )算法和兩段遞推最小二乘?偽逆( rls ? pi )算法等。