# fuzzy input to Neural network

From: MG (learnts@yahoo.com)
Date: Wed Dec 19 2001 - 09:09:45 MET

• Next message: Ho-Ling Poon: "Re: Fuzzy Clustering"

I am exploring the possibility of using fuzzy inputs to neural
network.
The output of NN model depends on some *quantitative* as well as some
*qualitative* data. The qualitative data can be roughly approximated
by using some boolean logic.
e.g. excellent= 1: when the data is in the range 81-100
very good=1: when the data is in the range 61-80
good=1: when the data is in the range 41-60
bad=1: when the data is in the ragne 0-40

I have thought of one possible use of boolean logic as input into the
network. The inputs of NN model are then clear to me : consists of
some *qunatitative* variables and some *boolean* variable.
e.g. with 6 inputs(4 boolean),one pattern may look like(if the
*qualitative* data has a numerical value of 81)
INPUTS OUTPUT
1 2 3 4 5 6 1
0.78 0.59 1 0 0 0 0.69

But clearly the classification boundaries here are very fuzzy. To
assign '1' as excellent when the input data has a value of '81' and
'0' when the input data has a value of '80' will not be so justful.
That is why I am pondering to seek towards fuzzy set and fuzzy logic
for help.
Now,when we use fuzzy set theory, by defining membership function we
may end up having some values in 'excellent' and some values in 'very
good' for the same value of '81' and '0' for good and bad classes. Say
for example we may have following inputs,
INPUTS OUTPUT
1 2 3 4 5 6 1
0.78 0.59 0.9 0.3 0 0 0.69
We may also use normalized values for input'3' and'4' and make the sum
as 1 instead of 1.2.

* Is this method valid? If not how can I input these values having
fuzzy boundaries in NN model?
If the NN model can perform well in the training and
cross-validation/test phase then one might assume that the method
worked. But, I wonder if my basis of work(In the framework of NN) is
incorrect or questionable then having excellent result would have no
meaning.
* Are there some good sites in the web addressing coupling of fuzzy
logic and neural network(e.g. some good tutorials to start with), or
some good books which are good as 'self-study'materials.

Mg

############################################################################
This message was posted through the fuzzy mailing list.
(1) To subscribe to this mailing list, send a message body of
"SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at
(2) To unsubscribe from this mailing list, send a message body of
"UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"
to listproc@dbai.tuwien.ac.at
(3) To reach the human who maintains the list, send mail to
fuzzy-owner@dbai.tuwien.ac.at
(4) WWW access and other information on Fuzzy Sets and Logic see
http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info
(5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html

This archive was generated by hypermail 2b30 : Wed Dec 19 2001 - 09:13:32 MET