A Method for Identification of Discrete Parametric Models with Unknown Orders and Delays

本論文提出一新型鑑別方法，可在開環或閉環操作下鑑別出受雜訊干擾系統之離散參數模型。該法應用一時間加權離散濾波器將特定時段內取樣的輸出入訊號轉換成一實數，然後利用不同時段的量測訊號以移動區間遞迴最小平方演算法來估測模型參數。針對待鑑別動態模型之階次與時延常為未知的情況，本文亦提出一簡單有效的技術來決定模型的階次與時延。與傳統最小平方法不同的是，本法能排除各種雜訊的影響而獲得參數的無偏估測，並且在模型結構不正確或取樣時間選擇不佳的情況下，仍具有良好的鑑別效能。

關鍵字

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並列摘要

A novel method is presented to identify a discrete parametric model of a noisy system under open-loop or closed-loop operation. The order and time delay of the dynamic model are assumed to be unknown a priori. A time-weighted digital filter is employed to group of linear algebraic equations. The moving horizon least-squares algorithm is then developed to estimate the model parameters in a recursive fashion. A very effective technique is also proposed to infer the model order and time delay from the observed data. In contrast with the conventional least-squares approach, the proposed method is able to yield unbiased parameter estimates despite the nature of noise. Furthermore, it is robust with respect to model structure mismatch and the selection of sampling period.

並列關鍵字

Unbiased identification ； Correlated residuals ； Time-weighted digital filter ； Discrete parametric models ； Model structure determination

國際替代計量

A Method for Identification of Discrete Parametric Models with Unknown Orders and Delays

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