Re: fuzzy logic and probability

Warren Sarle (saswss@hotellng.unx.sas.com)
Fri, 12 Jul 1996 13:58:36 +0200


In article <4rvl3a$lbi@news.tuwien.ac.at>, az@diabas.tuwien.ac.at (Alfred H. ZETTLER) writes:
|> The combination of Neural Networks and Fuzzy Logic is discussed in
|> Stephen T. Welstead's book "Neural Network and Fuzzy Logic applications
|> in C/C++" published by John Wiley & Sons Inc. 1994.

That is a pathetic excuse for a book. Read instead:

Brown, M., and Harris, C. (1994), Neurofuzzy Adaptive
Modelling and Control, NY: Prentice Hall.

Welstead is to Brown & Harris as a Corvair ("Unsafe At Any Speed")
is to a Mercedes.

Here is an excerpt from the comp.ai.neural-nets FAQ
(ftp://ftp.sas.com/pub/neural/FAQ4.html) regarding neural net books:

The Worst

Blum, Adam (1992), Neural Networks in C++, Wiley.

Welstead, Stephen T. (1994), Neural Network and Fuzzy Logic
Applications in C/C++, Wiley.

Both Blum and Welstead contribute to the dangerous myth that any
idiot can use a neural net by dumping in whatever data are handy
and letting it train for a few days. They both have little or no
discussion of generalization, validation, and overfitting. Neither
provides any valid advice on choosing the number of hidden nodes.
If you have ever wondered where these stupid "rules of thumb" that
pop up frequently come from, here's a source for one of them:

"A rule of thumb is for the size of this [hidden] layer to be
somewhere between the input layer size ... and the output layer
size ..." Blum, p. 60.

(John Lazzaro tells me he recently "reviewed a paper that cited this rule of
thumb--and referenced this book! Needless to say, the final version of that paper
didn't include the reference!")

Blum offers some profound advice on choosing inputs:

"The next step is to pick as many input factors as possible that
might be related to [the target]."

Blum also shows a deep understanding of statistics:

"A statistical model is simply a more indirect way of learning
correlations. With a neural net approach, we model the problem
directly." p. 8.

Blum at least mentions some important issues, however simplistic his
advice may be. Welstead just ignores them. What Welstead gives you is
code--vast amounts of code. I have no idea how anyone could write that
much code for a simple feedforward NN. Welstead's approach to
validation, in his chapter on financial forecasting, is to reserve
two cases for the validation set!

-- 

Warren S. Sarle SAS Institute Inc. The opinions expressed here saswss@unx.sas.com SAS Campus Drive are mine and not necessarily (919) 677-8000 Cary, NC 27513, USA those of SAS Institute.