Introduction - If you have any usage issues, please Google them yourself
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.