FxLMS(Filtered-X Least Mean Square) 演算法已經廣泛運用在主動噪音控制 (Active Noise Control,ANC)，它在一般高斯的情況下會有良好的收斂效果，已經有許多研究在討論如何改善它的效能，然而在實際應用中，ANC 系統會接收突然產生的異常訊號，我們稱之為脈衝噪音 (Impuslive Noise,IN)，這可能會使得傳統的FxLMS 演算法性能會嚴重下降或收斂失敗，過去有一些文獻已經有在討論這個部分，但許多提出的解決方案都是需要依賴預先確定的參數，在本文中，對於脈衝噪音問題主要採取截尾平均型方法 (Trimmed Mean)，他是來自於順序統計原理，新提出的 FxLMS演算法跟過去文獻不同，已不需要利用事前條件來定義參數，透過模擬結果，我們將跟過去的方法做比較，所提出的演算法具有更好的 (Average Noise Reduction,ANR) 性能。

The Filtered-X Least Mean Square (FxLMS) algorithm has been widely used for Active Noise Control (ANC), which has a satisfying convergence performance in the Gaussian noise. Hence, there have been many researches studying how to improve its performance in the literature. Nevertheless, in practical applications, the ANC system can receive an unusual signal suddenly generated and called the impulsive noise (IN),which can result in serious performance degradation or convergence failure to the conventional FxLMS algorithm. Some references can be found discussing this issue, but many of the proposed solutions highly relies on the setup of pre-determined parameters dependent of special stituations for the modified FxLMS algorithms. In this thesis, the IN problem is dealt with mainly applying the trimmed mean method, which is adapted from order statistics. With the new FxLMS algorithm, there is no prerequisite to determine the parameters as required in the previous works.
Through simulation results, we will show that the proposed method has better average noise reduction (ANR) performance compared to previous works.

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