Re: Advantage

Dmitry A. Kazakov <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>...
> On 1 Oct 2003 11:54:53 -0700, [EMAIL PROTECTED] (Bruno) wrote:
> 
> >Nobody has???
> 
> Neural networks provide a way to represent and organize data. Fuzzy
> logic is an extension of the conventional logic. What is to compare
> here? Absolutely nothing! A valid comparison could be neural vs. fuzzy
> neural networks. But it would be just crisp vs. fuzzy.
> 
Actually, neural networks can provide a large number of functions,
including (for example) classification. Thus for certain problems one
can use either a neural net or a fuzzy expert system. They are, of
course, quite different techniques.

An advantage of neural nets is that little or no a priori knowledge is
required; the corresponding disadvantage is that after the neural net
is constructed and tuned, one has little or no idea how it reaches its
conclusion. There has been a lot of work into extracting a rule base
that corresponds to the neural net from the neural net connections,
but I have the impression that the results of this work are not
terribly satisfactory. However, I am not up to date on this work, so
what I say may not be correct.

A disadvantage of neural nets is that a training set of data and the
corresponding conclusions is abolutely required. Some applications
cannot possible meet this requirement.

Expert systems do not usually require a training set of data for their
construction; instead, expert kowledge is used to construct the rules.
They do, of course, require calibration of model parameters and
subsequent validation on real-life data. Neural nets also require
validation.

This is a very short treatment of the subject, but is perhaps better
than nothing. I know of no publication that deals with the topic.

William Siler