Description: FIR filter, also known as finite impulse response filter, it uses current and past input samples to form the weighted sum of the value of its output, as the feed-forward differential equations described. FIR filter, also known as moving average filters, because any point in time are dependent on the output of M contains the latest input sample values a window. Because of its response depends only on a limited input, FIR filter to a discrete event there is a finite non-zero impulse response, that is, an M-order FIR filter to an impulse response in the M clock cycles after the zero.
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