HDTenEightyP/NetNews huggingface dataset:
From: erik@maui.til.com (Erik Horstkotte)
Subject: Re: Fuzzy logic references wanted
Keywords: fuzzy logic references
Organization: Togai InfraLogic, Inc.
Date: Mon, 28 Sep 1992 16:29:23 GMT

In article <1992Sep26.231551.3229@wixer.cactus.org>,
sparky@wixer.cactus.org (Timothy Sheridan) writes:

|> It has never been clear (ie fuzzy) to me what the distinction
|> between neural-
|> nets and fuzzy logic is.  However my limited understanding tels me
|> that they
|> are too close to be usfully separated.
|> I personally feel that AI alreaty has too many arbitrary
|> encampments that
|> stand by this or that techneque to exclusion of other usfull work.
|> if fuzzy logic is really different than neural_logic then we may
|> want to
|> create comp.ai.fuzzy-wuzzy-logic or alt.ai.heratical as well.

About the only *similarity* between fuzzy logic and neural networks
is that they both operate on numbers.  Structurally, they're about as
different as they could possibly be.  Fuzzy logic is an extension of
Boolean logic that handles truth values in the interval [0, 1]
instead
of the set { 0, 1 }.  A neural network is *not* a system of logic,
it's an architecture for computation.

I presume that what you're really talking about are fuzzy expert
systems, which are rule-based expert systems that operate on numbers
(rather than symbolic data) and are based on fuzzy logic instead of
Boolean logic.  Again, the only similarity is that they both operate
on numbers.  Fuzzy expert systems are actually far more closely
related to conventional expert systems.

On can (pretty obviously) construct neural networks that approxiately
duplicate a fuzzy expert system's I/O mapping, but that misses the
fundamental point that the two approaches are used under different
circumstances.

* Neural networks are used where one is not really interested in
  *now* decisions are made.  This is the classical black-box
approach.

* Fuzzy expert systems, on the other hand, are used in cases where
  *how* the decisions are made is important.  Such cases arise 
  whenever one needs to construct the expert system without a source
  of "right answers."
-- 
Erik Horstkotte, Manager, Software Development, Togai InfraLogic,
Inc.
erik@til.til.com, uunet!til!erik - (714) 975-8522
Standard disclaimer: the preceding are my opinions, not TIL's.
Newsgroups: comp.ai.neural-nets
From: clin@eng.umd.edu (Charles Lin)
Subject: How does one reason about NN's?
Date: Tue, 06 Oct 92 04:00:53 GMT
Organization: College of Engineering, University of Maryland, College Park
Reply-To: clin@eng.umd.edu (Charles C. Lin)


    Here's a question (from a novice) that's been bothering me?  How
does one reason about NN's.  By that, I mean how does one know what
when a neural network will train or not (say a backpro, or whatever)?
When I was attempting to train some stuff a few months ago, and asked
for suggestions on getting stuff to train, there were some solutions
that resembled "I tried this and it worked".   Is it possible that
NN's are too mathematically difficult to properly reason as to whether
it will converge or not?  I would think that some fokls whose
background amy be more formal may just shun NN's because they
are difficult to reason about (what makes an NN "program" "correct").

--
Charles Lin
clin@eng.umd.edu
From: rickert@mp.cs.niu.edu (Neil Rickert)
Newsgroups: comp.ai.neural-nets
Subject: Re: explanability of neural nets...
Date: 5 Oct 92 18:00:38 GMT
Organization: Northern Illinois University

In article <1992Oct5.155203.14434@m.cs.uiuc.edu> yang@milton.cs.uiuc.edu (Der-Shung Yang) writes:

>                                           It is my intention to 
>develop a neural net that can "explain" how it reaches a conclusion in
>a human-comprehensible way.

Keep in mind that most humans cannot "explain" how they reach a conclusion
in a human-comprehensible way.
From: andrzej@bnr.ca (Andrzej Bieszczad)
Newsgroups: comp.ai.neural-nets
Subject: Re: Help!!!
Date: 1 Oct 92 21:16:03 GMT
Followup-To: yhsim@memstvx1.memst.edu
Organization: Bell-Northern Research Ltd.,

In article <1992Oct1.111307.3471@memstvx1.memst.edu> yhsim@memstvx1.memst.edu writes:

	   This is my 1st time taking any Neural Networks course.  I wanted
   to do a computer project for my class, but I have no ideas what kind of
   program can I write?  (I mean the kind of applications, problems....)

	   If anybody has any suggestion I would be glad to listen, and thanks
   a lot.

   Yan Sim

Assume that you have a stream of patterns. Now you want to classify
the patterns into several categories. That would be easy (depending on
a few things; many NNs already do it), but make the problem a little
more complex. The patterns that you receive are spatial. Now, let us
say that there is another, temporal, dimension. That is there are
second order, temporal patterns, or sequences of the first order
spatial patterns. The sequences might be inter-wound, so you cannot
assume that the next first order pattern in the input stream belongs
to a particular sequence - it may belong to any of them. The final
task is to classify the sequnces.

The real world application? An object generates some state data. The
first order categorization classifies the patterns of the state
variables as an overall state, condition, of the object. The second
order processing categorizes the the nature of state changes. That is
usually the case in managing any type of hardware that generates
status records. If the first order categorization can detect a problem
it is great. But sometimes there are problems even if everything looks
fine at any given point in time.

Note, that there might be "big" gaps between two elements of a
sequence. There is also no word on the length of the sequence.
Therefore the task is more complex than those that can be treated
by TDNNs.

Let me know how far did you go.



--
========================================================================
Andrzej Bieszczad                     | Phone:  (613) 763-2259
Bell-Northern Research, Ltd.          | Fax:    (613) 763-3283
Dept. 9K12, P.O. Box 3511, Station C, | USENET: uunet!bnrgate!eh!andrzej
Ottawa, Ontario, Canada, K1Y 4H7      | BitNet: andrzej@BNR.CA
========================================================================
From: lee@boulez.CNMAT.Berkeley.EDU (Michael Lee)
Newsgroups: comp.ai.neural-nets
Subject: Fuzzy Logic and User Interfaces
Keywords: Fuzzy Logic User Interfaces
Date: 5 Oct 92 02:04:04 GMT
Organization: Silicon Graphics, Inc.
NNTP-Posting-Host: boulez.cnmat.berkeley.edu

Hi,

I am trying to find information on the application of Fuzzy Logic to
User Interfaces.  If you have any references or contacts, please send
them to me.

Thank You,

Michael Lee
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: ANNs and Predicate Calculus?
Organization: University of Alberta, Edmonton, Canada
Date: Mon, 5 Oct 1992 18:42:11 GMT

"Neuron-Request" <neuron-request@cattell.psych.upenn.edu> writes:
>   We would like to know if there has been any work on Predicate Calculus
>and neural nets. Specifically has there been any work on implementation
>of Unification on a connectionist network ? We find the question of 
>what is meant by variable binding on a neural net a particularly
>difficult one.
>                Thanking you,
>                       Pushpak Bhattacharyya

One way to view the connection between predicate logic and ANNs is via
adaptive logic networks (ALNs).  An ALN computes a boolean function,
not a function with a continuous range of values.  The limitation to
boolean values is overcome if one uses operations giving rise to
boolean values at the inputs, such as comparisons ( e.g.  x > 15.2).
Then we get predicates, as you requested.  From there, we can deal
with the predicates much as in PROLOG.  The usual functional approach
of neural networks, computing y = f(x), now becomes evaluation of a
predicate P(x,y).  To find y, given x, we need a restriction that
allows only negative thresholds on y ( i.e. y < const) to be used in
computing P.  Then, given x, we can find y by using binary search
guided by evaluations of the predicate P; y is the minimum value of w
such that P(x,w) = 0.  We need one predicate evaluation for each bit of
precision of y.

More generally, once we have predicates, we can then form constraint
nets among the variables in our problem.  This idea may seem very
strange to many people in ANNs, but some relationships are simply not
functional.  Information about the relationship is best represented as
a relation or predicate rather than a function.

I think I'll pass on the part of the question dealing with
unification, since unification usually involves function symbols, and
function symbols don't fit well into the above framework.  Besides
that, what is a substitution when you have no function symbols?  I
have the idea that it is just a form of constraint on the variables.
(Since writing this, I talked briefly with an expert and he confirms
that eliminating function symbols can cause problems in unification;
but we couldn't straighten the whole question out.)

Your question is very interesting.  I hope this gives you some ideas.

Bill



--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: alfred@dec25.lnt.e-technik.tu-muenchen.de (Alfred_Nischwitz)
Subject: Paper: A network to velocity vector-field correction
Keywords: competitive network, velocity vector-field correction
Organization: Institut fuer Medizinische Psychologie, Ludwig-Maximilians-Universitaet 
Date: Tue, 6 Oct 1992 10:39:24 GMT


The following paper has been published in the
proceedings of the International Conference on 
Artificial Neural Networks '92 in Brighton:

	Relaxation in 4D state space - A competitive network
	approach to object-related velocity vector-field correction


by  Helmut Gluender       Institut fuer Medizinische Psychologie
                          Ludwig-Maximilians-Universitaet
                          Goethestrasse 31, D-8000 Muenchen 2, Germany

and Astrid Lehmann        Lehrstuhl fuer Nachrichtentechnik
                          Technische Universitaet Muenchen
                          Arcisstrasse 21, D-8000 Muenchen 2, Germany

ABSTRACT:

A standard principle of (energy-)minimization is applied to the 
problem of visual motion analysis. In contrast to well-known 
mathematical optimization procedures and universal optimizing
networks it is proposed to use a problem-adapted network
architecture. Owing to the bilocal coincidence-type motion
detector considered here the task of object-related motion
analysis appears as a geometric correspondence problem. Hence,
the correct spatio-temporal correspondeces between elements in 
consecutive images must be selected from all possible ones. This
is performed by neighborhood operations that are repeatedly 
applied to the instantaneous signal representation in the 
space/velocity-domain until an estimate of the actual flow-field
is reached.

Hardcopies of the paper are available. Please send requests 
to the following address in Germany:

	Helmut Gluender  
	Institut fuer Medizinische Psychologie
        Ludwig-Maximilians-Universitaet
        Goethestrasse 31, D-8000 Muenchen 2, Germany

or via email to:
        alfred@lnt.e-technik.tu-muenchen.de

communicated by Alfred Nischwitz
From: street@goat.cs.wisc.edu (Nick Street)
Newsgroups: comp.ai.neural-nets
Subject: no, seriously: ART-2 code?
Date: 1 Oct 92 20:23:46 GMT
Organization: University of Wisconsin, Madison -- Computer Sciences Dept.


Sorry to be repetetive, but the search for good public domain source
code of an ART-2 simulator continues.  The only reference I've seen on
the net for the past year or so it to the art_code.ann code at
me.uta.edu.  This code is undocumented, buggy, and damn hard to
correlate with any of Grossberg & Carpenter's papers.  Has anyone
actually used this code successfully?  Is there a better simulator
somewhere else?  Or should one resign oneself to either writing it
from scratch or using something else?


All pointers appreciated.  Useful ones will be summarized, if anyone
else is interested.



-- 
------------------------------------------------------------------------
Nick Street				| "Go Make Plans"
street@cs.wisc.edu			|	-Bob
Newsgroups: comp.ai.neural-nets
From: thoeny@ito.umnw.ethz.ch (Juerg Thoeny)
Subject: E-Mail Address of J.A. Freeman/D.M.Skapura
Organization: ETHZ, Switzerland
Date: Mon, 28 Sep 1992 08:04:26 GMT

I'am looking for the E-Mail address of J.A. Freeman or D.M Skapura.

Cheers jeurg
From: yoshio@tuna.cis.upenn.edu (Yoshio Yamamoto)
Newsgroups: comp.ai.neural-nets
Subject: References to financial modeling/forecasting
Date: 6 Oct 92 17:31:22 GMT
Organization: GRASP Lab, University of Pennsylvania


Hi netters,

I am currently preparing a paper on financial forecasting using neural
networks, more specifically using cascade-correlation.  
I am looking for the references for the applciations of neural networks
in financial modeling and forecasting.  I've found lots of references in
nonlinear time series prediction, but only one in financial modeling so far. 
That is the one recently posted to the connectionist mail by the author of
University College London.  Your cooperation will be greatly appreciated.



Yoshio Yamamoto

General Robotics and Active Sensory Perception Laboratory
University of Pennsylvania


Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: mgundeko@ad-6.Naitc.Com (Murthy Gundekota)
Subject: Re: Fuzzy logic references wanted
Organization: Somewhere near Chicago
Date: Tue, 6 Oct 92 23:35:15 GMT

In article <1992Sep30.155618.5972@til.til.com> erik@maui.til.com (Erik Horstkotte) writes:
>o There is a strong cultural bias among engineers world-wide against
>  the term "fuzzy."  We've all been taught to be good, crisp, logical
>  thinkers, and to attack ill-defined situations by trying to make
>  them better-defined.  The notion that dealing with the vagueness
>  directly might yield better results is directly in opposition to
>  our training.

Thanks for a series of clarifications I wouldn't have thought of on my
own, even though I've background in control theory. This rigid
attitude, probably because of the mathematical origins, among control
engineers, especially those in a major engineering fields such as
chemical, mechanical, electrical, etc., causes them to reject
non-Fuzzy theories of AI. I've known cases where those using fuzzy
theory, argue against its alternatives (say Dempster-Shafer). I think
those who are in the applications side, and stubbornly dedicated to
predicate logic (or ladder logic say) for solving various ill-defined
control problems (some even question the notion of ill-defined
problems, because they don't think there is such a thing), have not
much choice when they do not fully succeed in solving their problems,
other than trying to improve their respective theories or simplifying
their problem definitions (now that every body does). However, I don't
see any reason why people using fuzzy logic have to view its
alternatives as less preferable. I think that is scary, given the lack
of content in the fuzzy logic, at the domain level, that is upto the
mark of most engineering practitioners, including students. Unlike
math, we are not brought up through high schol thinking in fuzzy
terms. So, it seems, fuzzy means "hand-waving," especially at grad
school level. Although, I want to view fuzzy as "beside" math or
logic. This is not to say, we can state and solve all engineering
problems using math and logic, much to the chagrin of most linear and
non-linear programming specialists. Finally, this leads to the
question of what is a good AI theory for engineering applications? I
think a good AI theory should allow domain content to be represented
and reused (as opposed to used once or compiled). And there has to be
a process of verification and proof built on top of the AI model. I
will stop at that and ask anyone who is interested to correspond by
e-mail.

Regards,

Murthy
Newsgroups: comp.ai.neural-nets,sci.engr.control
From: alanm@dres.dnd.ca (Alan McCormac)
Subject: Address of Nestor (US company)?
Keywords: neural networks, control systems
Reply-To: alanm@dres.dnd.ca
Organization: Defence Research Establishment Suffield
Date: Wed, 30 Sep 92 19:47:37 GMT

Hi folks:

	I'm new to the process of Usenet posting, so I hope I don't #$%^ up.
I am a mechanical engineer by training, wth a M.Sc. in the controls area, and
I work as a Defence Scientist.  Management here has suggested we look at the 
applications of neural networks to control of military vehicle systems.

	Someone suggested to me that a company called Nestor (I hope?) is a
terrific source of applications experience in neural nets.  We are not that 
interested in "neural nets can probably solve the world's problems" views as
much as in "we made a neural network control an electro-mechanical pan-tilt 
head to track an object with 0.1 degree precision" type of papers/reports.

	If you know of the a/n company or of anyone else into real-time 
control applications of neural networks, or if you know enough about them 
to tell me concretely that I'm barking up the wrong tree, I would appreciate
e-mail information to the address in my signature. (I hope our mailhost is 
back on-line by the time I post this.)

	Atdhvaannkcse for any help!


-- 

Alan McCormac (DND/DRES/DTD/MES/VCG)                       alanm@dres.dnd.ca
(403) 544-4832     DRE Suffield, Box 4000, Medicine Hat, AB, Canada, T1A 8K6
From: yhsim@memstvx1.memst.edu
Newsgroups: comp.ai.neural-nets
Subject: Help!!!
Date: 1 Oct 92 11:13:07 -0600
Summary: Computer project!!
Reply-To: yhsim@memstvx1.memst.edu
Followup-To: yhsim@memstvx1.memst.edu
Distribution: world
Organization: Memphis State University

Hi! everybody:

	This is my 1st time taking any Neural Networks course.  I wanted
to do a computer project for my class, but I have no ideas what kind of
program can I write?  (I mean the kind of applications, problems....)

	If anybody has any suggestion I would be glad to listen, and thanks
a lot.

Yan Sim
From: roat@rrc.chevron.com (Suzanne Roat,Chevron,(510)242-5313)
Newsgroups: comp.ai.neural-nets
Subject: Re: Has making a group for Genetic Algorith
Date: 1 Oct 92 21:38:22 GMT
Reply-To: roat@rrc.chevron.com
Organization: Chevron, Richmond, California

In article 92Oct1131144@bull.shizuoka.ac.jp, purna@shizuoka.ac.jp (Yusuf Wilajati Purna) writes:
>I don't know whether or not comp.ai.ga exists. However, there has been
                             ^^^^^^^^^^
>already a moderated mailing list for GA. To join the mailing list, you
>could send an e-mail request to:
>
>	GA-List-Request@AIC.NRL.NAVY.MIL
>
>I hope this will be helpful.
>
>-- Wila --

Genetic algorithms range more widely than AI.  I would vote for comp.ga or
sci.math.ga or just sci.ga.

---
Suzanne Roat				Phone:	(510) 242-5313 Voice 
Chevron Research and Technology Company		(510) 242-4647 FAX
100 Chevron Way				Email:	roat@rrc.chevron.com
Richmond, CA  94802-0627

Newsgroups: comp.ai.neural-nets
From: sdhar@uceng.UC.EDU (Suman Dhar)
Subject: test
Organization: College of Engineering, University of Cincinnati
Date: Thu, 1 Oct 1992 00:24:06 GMT


This is a test
Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: erik@maui.til.com (Erik Horstkotte)
Subject: Re: Fuzzy logic references wanted
Organization: Togai InfraLogic, Inc.
Date: Wed, 30 Sep 1992 15:56:18 GMT

In article <2gf.h0hrp@lab.ntt.jp>, farzin@apollo3.apollo3.ntt.JP
(Farzin Mokhtarian) writes:

|> Perhaps you are aware that the inventor (Prof. Lotfi Zadeh) is
|> Iranian.
|> Unfortunately, it seems that may have had something to do with it.

I've seen precisely *no* evidence that prejudice against Iranians
has had anything to do with the slow acceptance of fuzzy logic in
the USA or Europe.  In fact, in my experience in doing presentations
to very difficult customers, the major obstacles I have found are:

o The people in the USA who develop control systems generally have
  formal training in conventional linear control theory.  Many of
  the people in Japan (at least) working in the same field have
  their training in other areas.  This means that the Americans
  suffer from the limitations of their training, whereas the 
  Japanese don't have the strong linear control tradition to get
  in the way of trying new things.

o Most people trained in control engineering in the USA have been
  taught that nonlinear control systems cause instability.  This
  just isn't true of course.  They *can* cause it, just as a
  linear control system can cause instability.

o There is a strong cultural bias among engineers world-wide against
  the term "fuzzy."  We've all been taught to be good, crisp, logical
  thinkers, and to attack ill-defined situations by trying to make
  them better-defined.  The notion that dealing with the vagueness
  directly might yield better results is directly in opposition to
  our training.

  As a historical side note that may illuminate the exact nature of
  the problem, Fuzzy logic was originally introduced in Japan under
  the name "Aimai logic" which is a fairly literal translation.  It
  went over like a lead balloon.  Later it was reintroduced as
  "Fujji logic," and has gone on to wide acceptance and great
  success.

  Almost every time he does a talk about fuzzy logic, Dr. Zadeh
  apologizes for the problems that his choice of term has caused,
  but as he says, it's descriptive, and no one has come up with a
  better term (as far as I have heard).
-- 
Erik Horstkotte, Togai InfraLogic, Inc.
erik@til.til.com, uunet!til!erik - (714) 975-8522
Standard disclaimer: the preceding are my opinions, not TIL's.
Newsgroups: comp.ai.neural-nets
From: u8013536@u8013536.cc.nctu.edu.tw (Yuanfu Liao)
Subject: Hardware of neural network
Keywords: hardware
Organization: NCTU
Date: Tue, 6 Oct 1992 17:31:29 GMT

Hello,

  I implement by software the multi-layer-percepron with digital weights
(8-bits) and the step function. I train it to be a pattern classifier by 
GAs. And its performance is not bad, comparing with those trained by 
backpropagation method(3% lower only).

  My case is a full-connected single hidden layer perceptron with ten input 
nodes, ten hiddenneurons and five output neurons. So it uses only 1240-bits
[ (10*10+10*5+5)*8 ] memory, fifteen adders, ten comparers and a winner-
take-all circuit.

  So, I think is is very easy to implement it by hardware with the general 
ditigal-IC technique. But I doubt if this way is better than the general
analog implements ?

  Could anyone tell me some information about this and any product of neural 
network ? Or Do anyone do the same thing as me ?

  Thank in advance.

  Yuanfu Liao
  u8013536@cc.nctu.edu.tw
Newsgroups: comp.ai.neural-nets
From: drew@cs.toronto.edu (Drew van Camp)
Subject: Re: Scientic American Sept 92, code anybody?
Organization: Department of Computer Science, University of Toronto
Date: 30 Sep 92 15:08:45 GMT

In article <26408@castle.ed.ac.uk> tmw@festival.ed.ac.uk (Tom WRight) writes:
>Does any body have the code for the amateur scientist 
>article in the Sept issue of Sci Am.
>
>
>Thanks

I've put the source code for the simulator I used in the article up
for anonymous ftp. It's a *very* primitive simulator, but it does the
job. It's written in C and should compile with any ANSI C compiler.
You can get it from:

	ftp.cs.toronto.edu:/pub/xerion/sciam.tar.Z

If you are interested in a much more powerful (and more complex)
simulator have a look at the file:

	ftp.cs.toronto.edu:/pub/xerion/README-3.0

Drew van Camp

/--- Drew van Camp --------------- drew@cs.toronto.edu-----------\
| Dept. of Computer Science, University of Toronto               |
| 6 Kings College Road, Toronto, Ontario   Voice: (416) 978-7403 |
| CANADA M5S 1A4                           Fax:   (416) 978-1455 |
\----------------------------------------------------------------/
Newsgroups: comp.ai.neural-nets