tracking adaptive algorithm

On Apr 11, 1:32 am, Vladimir Vassilevsky <antispam_bo...@hotmail.com>
wrote:
> sopapo wrote:
> > Hi everyone,
>
> > I'm working on my phd thesis in a field related with the active noise
> > control and i'm looking for an algorithm with good tracking capability, to
> > work with a time-varying input signal (non stastionary).
>
> Excuse me, but what is exactly new in your thesis ?
>
> By definition, there is no way to track the non-stationary signal. In
> order to track the signal, you have to assume that it obeys some
> stationarity. Then it is down to the classic problem of Wiener/Kalman
> filter.
>
> > I have made several test with FXLMS, N-FXLMS, Leaky-FXLMS without much
> > succes, I'm going in wrong direction with this algorithms?
> > Variable-step-size-LMS could improve that tracking capability?
>
> *.LMS are inherently bad algorithms. They are not optimal and can't be
> made optimal by the black magic manipulations. The only justification
> for the *.LMS is the simplicity.
>
> Vladimir Vassilevsky
> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com

Actually a few years back there was a paper that proved that LMS is
optimal in the sense of H infinity.

K.

>>> *.LMS are inherently bad algorithms. They are not optimal and can't be
>> made optimal by the black magic manipulations. The only justification
>> for the *.LMS is the simplicity.
>>
>> Vladimir Vassilevsky
>> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com
>
>Actually a few years back there was a paper that proved that LMS is
>optimal in the sense of H infinity.
>
>K.
>

Hi K.,

do you remember who wrote the paper?

Manolis

On Apr 11, 7:16 am, "Manolis C. Tsakiris" <el01...@mail.ntua.gr>
wrote:
> >>> *.LMS are inherently bad algorithms. They are not optimal and can't be
> >> made optimal by the black magic manipulations. The only justification
> >> for the *.LMS is the simplicity.
>
> >> Vladimir Vassilevsky
> >> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com
>
> >Actually a few years back there was a paper that proved that LMS is
> >optimal in the sense of H infinity.
>
> >K.
>
> Hi K.,
>
> do you remember who wrote the paper?
>
> Manolis

 H({infinity} ) optimality of the LMS algorithm
Hassibi, B | Sayed, A H | Kailath, T
IEEE Transactions on Signal Processing. Vol. 44, no. 2, pp. 267-280.
Feb. 1996

We show that the celebrated least-mean squares (LMS) adaptive
algorithm is H({infinity}) optimal. The LMS algorithm has been long
regarded as an approximate solution to either a stochastic or a
deterministic least-squares problem, and it essentially amounts to
updating the weight vector estimates along the direction of the
instantaneous gradient of a quadratic cost function. We show that the
LMS can be regarded as the exact solution to a minimization problem in
its own right. Namely, we establish that it is a minimax filter: it
minimizes the maximum energy gain from the disturbances to the
predicted errors, whereas the closely related so-called normalized LMS
algorithm minimizes the maximum energy gain from the disturbances to
the filtered errors. Moreover, since these algorithms are central
H({infinity}) filters, they minimize a certain exponential cost
function and are thus also risk-sensitive optimal. We discuss the
various implications of these results and show how they provide
theoretical justification for the widely observed excellent robustness
properties of the LMS filter

K.

>On Apr 10, 5:15 pm, "sopapo" <sop...@sopapo.com> wrote:
>> Hi everyone,
>>
>> I'm working on my phd thesis in a field related with the active noise
>> control and i'm looking for an algorithm with good tracking capability,
to
>> work with a time-varying input signal (non stastionary).
>>
>> I have made several test with FXLMS, N-FXLMS, Leaky-FXLMS without much
>> succes, I'm going in wrong direction with this algorithms?
>> Variable-step-size-LMS could improve that tracking capability?
>>
>> Any advice is welcomed.
>> Thx.
>
>Submit for a Masters.
>
>K.
>

Try googling for a Wiener-Hopf adaptive algorithm? From the description,
I'm not sure exactly what you're trying to do. D