Hi,
The answer to your question is in a paper by Ali & Zhang, in March 2001
issue of Fuzzy Sets and Systems. This research was done six years ago, and
similar work has been reported in the same journal for the last 15 years.
Basically, the answer is: You can define a generic membership function, and
"fine tune" it to a shape proper for your data. In doing so, you will be
effectively doing the "learning" stage of a neural network model.
"Kuentai Chen" <kchen@taylor.ie.ksu.edu> wrote in message
news:200102191937.NAA16104@mailhub.cns.ksu.edu...
> Hello,
> I have a very basic question for the use of fuzzy logic: If I have a
> set of data on hand, how do I know what membership to use or if
> triangular membership function will be good enough?
> Has any research been done in this field? I would appreciate if
> anyone can tell me any reference available.
> Thanks in advance.
>
>
>
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