I am a little confused by your ppost. You seem to be saying that
since the filter is 32 samples long, you are iterating over 32
samples. But I don't think that's what you mean. If you are asking
how many samples it will take for the filter to converge, then that is
an exponential exp[-1/(mu * lambda(m))], where mu is the gain, and
lambda(m) is the minimum eigenvalue. The number of samples it will
take to converge is dependent on the sampling rate.


Maurice Givens


"Zach R." <[email protected]> wrote in message news:<yj3Qa.11308$[email protected]>.. .
> Hi all,
> I am currently trying to implement a simple LMS algorithm in C and on an
> analog deviced blackfin ez-kit. The only real problem is that I am trying
> to implement it in a streaming manner and as close to real time as possible.
> I have an implementation but it isn't working.
> I am wondering how many samples should I be looking at when estimating
> the gradient? The books I have been reading pretty much just use the
> samples in the filter e.g. the last 32 samples for a 32nd order filter.
> That is how I have been estimating the gradient, should it work?
>
> Thanks for your help,
> Zach