Re: learning algorithm for tuning fuzzy rules based on the gradient descent method

Hi,

I do not have this paper but if you Google for "gradient
descent method" you will find information about how this
technique is used in other domains. Searching the IEEE
Explorer it returned me IEEE Transaction papers.

I think that you should use a fraction of this error to modify
the rules (usually definition of the fuzzy sets of your system)
and this until this error get mimized (either limiting the
number of iterations or relative error from one iteration
to another very very small). That should be the basic concept.

Maybe this link can help you to understand better the way
this technique can be used.
http://www-math.cudenver.edu/~aknyazev/teaching/98/4660/fp/r2/

Hope this help.

Brahim

> The paper reference is 
> A learning algorithm for tuning fuzzy rules based on the gradient
> descent method Shi, Y.; Mizumoto, M.; Yubazaki, N.; Otani, M.; 
> Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International
> Conference on , Volume: 1 , 8-11 Sept. 1996
> Page(s): 55 -61 vol.1
> 
> assume the input is X = [ 12,13,14,15]
> and estimated output is y =[12.345, 13.215, 14.871, 15.654]
> we know that the error occoured is the difference 
> So 
> we have,
>    Y' = [0.345,0.215,0.871,0.654]
> how do we go about doing this  using the above?
> 
> any help with this will be appreciated
> thanks.