On Friday, October 13, 2017 at 7:34:30 PM UTC-5, ankita bombatkar wrote:
> Can anybody provide me the solution for following problem. It will be gre=
at help.
>=20
>=20
>=20
>=20
> Adaptive filter with variable update equation: Develop a stochastic gradi=
ent adap-tive =EF=AC=81lter that attempts to minimize the following cost fu=
nction:
> J(n)=3DE{|e^2(n)|}.         e(n)<1
>     =3DE{|e^3(n)|}.         e(n)>=3D1
>=20
> Discuss the possible advantages and disadvantages of your algorithm over =
the LMSadaptive =EF=AC=81

I forgot to add, what does the question mean by variable update equation? (=
hint: can the LMS update gain be written as a matrix?). There are numerous =
examples you can research to give you ideas. After you do this research, th=
ink hard about the advantages and especially the disadvantages of these var=
iable gain ideas. I hope you can come up with your own idea that mitigates =
the disadvantages.

If you come up with something, share it with us.

Maurice Givens

On Friday, October 13, 2017 at 7:34:30 PM UTC-5, ankita bombatkar wrote:
> Can anybody provide me the solution for following problem. It will be great help.
> 
> 
> 
> 
> Adaptive filter with variable update equation: Develop a stochastic gradient adap-tive filter that attempts to minimize the following cost function:
> J(n)=E{|e^2(n)|}.         e(n)<1
>     =E{|e^3(n)|}.         e(n)>=1
> 
> Discuss the possible advantages and disadvantages of your algorithm over the LMSadaptive &#64257;

Ankita,
Sounds like a homework problem. You have a cost function you want to minimize. Think about what process you can use to tell if you have a minimum or not (hint: there's a reason that E[e^2] is widely used). Expand the cost function, apply the process to the expansion, and then see if that doesn't lead you to the answer.

On Friday, October 13, 2017 at 5:34:30 PM UTC-7, ankita bombatkar wrote:
> Can anybody provide me the solution for following problem.
> It will be great help.

> Adaptive filter with variable update equation: Develop a
> stochastic gradient adap-tive filter that attempts to minimize
> the following cost function:
> J(n)=E{|e^2(n)|}.         e(n)<1
>     =E{|e^3(n)|}.         e(n)>=1

> Discuss the possible advantages and disadvantages of
> your algorithm over the LMSadaptive &#64257;

Discuss your thoughts so far, and I am sure you will get some
help here.  Do you have a partial solution, but got stuck
along the way?

Can anybody provide me the solution for following problem. It will be great help.




Adaptive filter with variable update equation: Develop a stochastic gradient adap-tive filter that attempts to minimize the following cost function:
J(n)=E{|e^2(n)|}.         e(n)<1
    =E{|e^3(n)|}.         e(n)>=1

Discuss the possible advantages and disadvantages of your algorithm over the LMSadaptive &#64257;