臺灣博碩士論文加值系統

近幾年來適應訊號處理有許多應用，吸引了許多研究人員的注意。例如聲學迴音消除、噪音控制等等。而適應訊號演算法中，因為最小均方根演算法(LMS)架構簡單，因此成為廣泛應用的演算法。但由於LMS演算法的濾波器系數長度固定，因此設定係數長度常常是費時費力，又在時變系統中，係數長度會跟隨時間變更，更使適應濾波器有應用上的困難。
在本論文中，我們提出的可變長度適應濾波器演算法，能夠根據誤差信號的函數迅速將濾波器長度至適合的大小，減少不必要的運算量，並且保有LMS精確估測的優點，在與其他可變濾波器長度的演算法比較中，也有優異的效能表現。

There are many adaptive signal processing applications which attract a lot of researchers in recent years. For example: the echo cancellation, noise control and etc. Because of the simplicity, LMS is very popular in the real applications. One of the important research trend of LMS is to study the variable length of the filter.
In the thesis, we propose a variable tap-length adaptive filter algorithm. It uses on the gradient of error signal to tune filter to suitable order. It is an improved GD algorithm. It has better performance than original GD algorithm in any aspect.

目錄
中文摘要.............................................. i
英文摘要............................................. ii
目錄................................................ iii
圖目錄.................................................v
表目錄.............................................. vii
第一章 簡介
1.1 研究背景...........................................1
1.2 論文組織...........................................4
第二章 可變長度濾波器介紹
2.1 簡介...............................................6
2.2 GD演算法...........................................7
2.3 FT演算法..........................................10
第三章 可變長度適應濾波器演算法
3.1 簡介..............................................12
3.2 可變長度適應濾波器演算法..........................12
第四章 實驗模擬與結果
4.1 簡介..............................................17
4.2 實驗一：可變長度適應濾波器演算法之驗證............18
4.3 實驗二：可變長度適應濾波器演算法與GD演算法以及FT演算法的比較..................................................22
4.4 實驗三：可變長度適應濾波器演算法與GD演算法以及FT演算法在較接近真實環境的系統參數比較..........................27
4.5 實驗四：可變長度適應濾波器演算法與GD演算法以及FT演算法在實際量測的真實環境系統參數的比較......................33
4.6 實驗總結..........................................39
第五章 結論...........................................41
參考文獻..............................................43

[1] Y. Gong, and C. F. N. Cowan, “An LMS style variable tap-length algorithm for structure adaptation,No. ” IEEE Transactions on Signal Processing, vol. 53, 7, pp. 2400-2407, July 2005.
[2] Y. Gu, K. Tang, and H. Cui, “LMS algorithm with gradient descent filter length,No. ” IEEE Signal Processing Letters, vol. 11, 3, pp. 305-307, March 2004.
[3] Y. K. Won, R. H. Park, J. H. Park, and B. U. Lee, “Variable LMS algorithm using the time constant concept,” IEEE Trans. Consumer Electron, vol. 40, no. 3, pp. 655-661, Aug. 1994.
[4] C. Paleologu, S. Ciochina, J. Benesty, “Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp.245-248, Mar. 2008.
[5] Z. Pritzker and A. Feuer, “Variable length stochastic gradient algorithm,” IEEE Trans. Signal Process, vol. 39, no. 4, pp. 997-1001, Apr.1991.
[6] M. R. Asharif, A. Shimabukuro, T. Hayashi, K. Yamashita, ” Expanded CLMS Algorithm for Double-Talk Echo Cancelling,” IEEE SMC ''99 Conference Proceedings, vol. 1, pp. 998-1002, Oct. 1999.