文件名称:FIR
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1、系统仅处于训练状态。训练序列为2cos(2π×f1×n/fs)，
干扰为sin(2π×f2×n/fs)；序列长度为100个样点。
其中n=0…99，f1=1kHz，f2=2kHz，fs=8kHz。
2、采用LMS算法，均衡器输入为训练序列加上干扰，均衡器阶数为21阶，步长因子β=0.01。
3、在Matlab中编写浮点LMS算法，绘制均衡器的输出波形，并与训练序列进行对比。
-1, the system only in the training status. The training sequence is 2cos (2πf1n/fs), interference sin (2πf2n/fs) sequence length of 100 samples. Where n = 0 ... 99, f1 = 1kHz, f2 = 2kHz, fs = 8kHz. 2, using LMS algorithm, the input to the equalizer training sequence plus interference equalizer order is 21 bands, step factor β = 0.01. 3, the preparation of the floating-point LMS algorithm in Matlab, draw the output waveform equalizer, and with the training sequence comparison.
干扰为sin(2π×f2×n/fs)；序列长度为100个样点。
其中n=0…99，f1=1kHz，f2=2kHz，fs=8kHz。
2、采用LMS算法，均衡器输入为训练序列加上干扰，均衡器阶数为21阶，步长因子β=0.01。
3、在Matlab中编写浮点LMS算法，绘制均衡器的输出波形，并与训练序列进行对比。
-1, the system only in the training status. The training sequence is 2cos (2πf1n/fs), interference sin (2πf2n/fs) sequence length of 100 samples. Where n = 0 ... 99, f1 = 1kHz, f2 = 2kHz, fs = 8kHz. 2, using LMS algorithm, the input to the equalizer training sequence plus interference equalizer order is 21 bands, step factor β = 0.01. 3, the preparation of the floating-point LMS algorithm in Matlab, draw the output waveform equalizer, and with the training sequence comparison.
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FIR.m