HDTenEightyP/NetNews huggingface dataset:
From: aumeier@engin.umich.edu (Steven E Aumeier)
Subject: ANN State of the Art ?
Date: Fri, 11 Sep 92 21:52:40 EDT
Organization: University of Michigan Engineering, Ann Arbor

	I have two general questions reqarding neural net 
state of the art:
	1) Does there exist a method by which we are
           able to systematically determine network
           topology (# of nodes and # of layers) for
           a feed-foreword perceptron-type net (not
           a net used for pattern classification where
           a decision tree is mapped onto a neural net).

	2) Does there exist a neural net structure that
           accounts for a priori knowledge of system equations
           (i.e. we may have some knowledge of how an
           input vector is related to a corresponding output
           vector).

	If anybody has an answer to these questions, I would certainly
appreciate hearing from you via E-Mail, the address is given below.

SE Aumeier
aumeier@syndicoot.engin.umich.edu

Newsgroups: comp.ai.neural-nets
From: styri@hal.nta.no (Haakon Styri)
Subject: Re: source code in "neural network pc-tools" book
Reply-To: styri@nta.no
Organization: Norwegian Telecom Research
Date: Mon, 14 Sep 92 13:07:53 GMT

In article <18r0ghINN5ra@matt.ksu.ksu.edu>, sunjain@matt.ksu.ksu.edu (mera bharat mahan) writes:
|>
|>   Has anybody taken the pains to write the source code for back-propagation
|> and kohenon algorithm given in the appendix A and B of "Neural Network PC Tools"
|> book?
|> 
|> I am not able to get hold of Dr. Dobbins to request for the diskette. 
|> ( I am just a beginner and need to do some assignments for back-propagation this
|>   weekend ! )
|> 
|> I'll appreciate if somebody who has written the code, can provide me.

Yup, I've seen at least one copy of this code on the net (ftp archives).
However, the code is copyright protected if I'm not too wrong. (That means
you may type it in from the book yourself, but you may not distribute your
copy of the code.)

The address for buying the diskette in the book is a bit out of date.
Try this instead:
			Software Frontiers Systems, Inc.
			P.O.Box 402
			Gilbert, Arizona 85234

			phone (602) 985-8550
			order phone 1-800-475-9082

The "Neural Network PC Tools Companion" is still US$ 20.00 as the good book says.
You can buy a toolkit including the `companion' for US$ 40.00 and use even more
money on a bunch of options. S&H US$ 3.00 (US$ 6.00 foreign).Ask SFS for product
information.

On the other hand, there are other software you can copy. Dr. Donald Tveter just
posted his fast back-propagation software. (Now, where's that list of "nn" s/w
and archives...)


Disclaimer:	I'm not employed by, nor do I have any interest in,
		Software Frontiers Systems, Inc.
		Just a satisfied customer speaking...

---
Haakon Styri	(I dont' speak on behalf of my employer either)
From: ps@tangtown.hanse.de (Peter Schwarz)
Newsgroups: comp.ai.neural-nets
Subject: WANTED : list of available neuro chips
Distribution: world
Date: Fri, 04 Sep 92 20:15:17 GMT
Organization: CeCa

Hello,
i would like to get a list of all available neuronal network chips
available. Very important are name of the chip and manufacturer.

Please send me a mail. I will sumarize then.

Thanks
       Peter

--

+-------------------------------------------------------+
|   Peter Schwarz        | Tel.: 49-4109-6420           |
|   ps@tangtown.hanse.de | Adr.: Claudiusstr. 22        |
|                        |       D-2000 Tangstedt       |
+------------------------+------------------------------+
From: tch@cis.ksu.edu (Thomas C Hampton )
Newsgroups: comp.ai.neural-nets
Subject: Code/Algorithms for NNs
Date: 8 Sep 1992 01:57:31 GMT
Organization: Kansas State University, Dept. of Computing and Information Sciences
Distribution: world
NNTP-Posting-Host: cygnus.cis.ksu.edu

Hi --

I am part of a group that is building a robot.  We are looking into
using a NN for controlling the balancing of this robot.  I have
several books about neural computing and such.  But, I am looking for
more specific details (i.e. code and/or data structures and/or
algorithms).  The language doesn't matter too much, but C, Pascal, or
FORTRAN would probably be preferred.  If someone could point me in the
write direction I would greatly appreciate it.  Oh, hopefully the code
would be well documented, so that I have some hope of understanding
what is being done and WHY. :-)  The books that I have been reading
discuss the theoretical aspects and present a lot of mathematical
gobbly-gook, but I need practicle knowledge.  

Also, a summary of the diferent types of NNs and their relative
adv/disadv would be great.  

Thanks in advance.

Tom

"Where would we be without the net?"

Newsgroups: comp.ai.neural-nets
From: pcm@cs.brown.edu (Peter C. McCluskey)
Subject: Re: NNs in chess or other games
Organization: Brown Computer Science Dept.
Date: Sat, 12 Sep 1992 18:23:30 GMT

In article <18psroINNnek@nestroy.wu-wien.ac.at>, bruhn@uxe.wu-wien.ac.at (Peter Bruhn) writes:
|> Does anyone of you know something about an application of neural nets
|> in strategic games (esp. chess). What I think about is that a NN could
|> be trained to evaluate a position or some aspects of a position (e.g.
|> the structure of your pawns, how safe is the position of your king,...).
|> 
|> The idea behind is that evaluating a position is (as I see it) closely
|> related to pattern-recognition. An experienced human chess player is
|> able to identify a position as weak, even though there are no tactical
|> dangers to perceive and he/she has not seen the position before. So how
|> does he/she know? I think it is due to pattern recognition: He/She has
|> seen similiar positions before or has made the experience that a certain
|> pawn structure is a weak point. Once a chess master (I don't remember his
|> name) was asked how many moves he prefigures in advance and he answered
|> "normally: none". Even though this is an exageration, of course, his

 I think the quote you are thinking of was from Capablanca, and was about
how many different continuations he looked at for a given position, the
answer being "Only one, but it is the right one", the implication being that
a sufficiently powerfull move generator will generate the most important
moves first, and looking at the first few that it generates is usually
sufficient. This pruning of the search tree enables good players to look
farther ahead; they probably average 5-10 moves deep.
 A well trained NN should be able to take a representation of the current
position as input and produce one or more "important" moves as output, for
use in deciding what parts of the search tree to evaluate, and should also
be able to output an evaluation of the position for use in comparing the
results of moves.
 The standard NN algorithms could be trained on targets such as moves from
typical grandmaster games or expert evaluations of the most interesting
moves from a given position. For evaluating how good a position is, you
would probably need to get expert evaluations.
 The standard algorithms could be supplemented with reinforcement learning
algorithms, such as having 2 different networks play each other, and
rewarding the winning network, punishing the loser.
 The biggest problem is likely to be choosing a good input representation
of the current position. I suspect a good representation will compare to
a straightforward 8 x 8 array of squares in much the same way that the
output of the human optical nerves compares to bitmapped graphics.

----------------------------------------------------------------------
  \\ !Vote Libertarian! \\ Peter McCluskey  \\ !Vote Libertarian! \\
  // Vote Marrou in `92 // pcm@cs.brown.edu // Vote Marrou in `92 //
----------------------------------------------------------------------
Newsgroups: comp.ai.neural-nets
From: fernando@eemips.tamu.edu (Susith Rohana Fernando)
Subject: Help: Techniques to analyze internal coding in trained NNs
Organization: Texas A&M University
Date: Wed, 9 Sep 1992 18:03:49 GMT

Hi Netters,

I am currently compiling the techniques available to analyze internal 
codings in trained neural networks. Any techniques used to analyze the 
features the networks extracted or techniques used to extract rules from NNs
that were trained to do pattern classifications, would count. The networks I 
am interested are multilayer perceptron, time-delay neural networks and simple 
recurrent networks(the ones proposed by Elaman and Jordan etc.). I would 
greatly appreciate any help with this. I will summarize the responses.

The following is one paper I have on this subject.

Gorman, R. Paul and Sejnowski, Terrence J.
Analysis of hidden units in a layerd network trained to classify sonar targets.
Neural Networks, vol. 1, pp 75-89, 1988.

Thank you in advance,

Susith Fernando
Dept. of Electrical Engineering
Texas A&M University
College Station, Tx. 77840
e-mail: fernando@eemips.tamu.edu
Newsgroups: comp.ai.neural-nets
From: s8818037@spectrum.cs.unsw.oz.au (Matthew James Leayr)
Subject: function approximation with nets.
Organisation: University Of New South Wales
Reply-To: s8818037@spectrum.cs.unsw.oz.au (Matthew James Leayr)
Organization: none
Date: Mon, 14 Sep 1992 05:34:08 GMT



Hi,
	I was wondering how one would go about training a network to perform
an operation such as conversion from x-y to polar coordinates. Do you simply
present examples of the conversion to the net in the training data? If so, 
how well suited are neural networks to this sort of task? Could a NN be used
to perform a log-polar coordinate transform?
	Also, does anyone have any information regarding size and rotational
invariance in neural networks?


Thanks.
Matt.

s8818037@spectrum.cs.unsw.oz.au
From: kimhock%Solomon.Technet.sg (Teo Kim Hock)
Subject: NN process emulator
Organization: National University of Singapore
X-Newsreader: Tin 1.1 PL5
Date: Mon, 7 Sep 1992 09:03:18 GMT




Hi,

We have been working on using the feed-forward NN to emulate the
dynamics of chemical processes.  It works well for simple processes but
fail to learn the process with non-linear time-delay, for example

  y(t) = u(t+10-int(y(t-1)/.1)*0.2 + y(t-1)*0.8
where 0<y(t)<1.0.

This process has time-delay that is a function of the output.  We have
tried using feed-forward net to learn the dynamics of this process
without any sucess.  Can anyone give us some advise on this.  Thank you
in advance.

Kim Hock. 


Newsgroups: comp.ai.neural-nets
From: (Michael Bruewer)mib@bio128.uni-bielefeld.de 
Subject: Re: cerebellum networks
Date: Mon, 7 Sep 92 09:46:20 GMT
Reply-To: mib@bio128.uni-bielefeld.de 
Organization: Universitaet Bielefeld

In article <IRH.92Sep7172029@leo.bby.oz.au> irh@leo.bby.oz.au (Ian R Heddle)  
writes:
> 
> Does anyone out there heard of cerebellum networks? Apparently these
> networks were developed at the University of New Hampshire by a Dr
> Aldus. Any hint's or clues as to these networks would be appreciated.
> Regards,
> Ian Heddle.
> --


Albus, JS (1975) A new approach to manipulator controls: the cerebellar model  
articulation controller (CMAC) J Dyn Syst Meas Control 97:270-277

_________________________________________________________________
Michael Bruewer

e-mail: mib@bio128.uni-bielefeld.de
 
Universitaet Bielefeld
From: bruhn@uxe.wu-wien.ac.at (Peter Bruhn)
Newsgroups: comp.ai.neural-nets
Subject: NNs in chess or other games
Date: 11 Sep 1992 10:36:40 GMT
Organization: Vienna University of Economics and Business Administration
Distribution: world
NNTP-Posting-Host: exaib.wu-wien.ac.at

Does anyone of you know something about an application of neural nets
in strategic games (esp. chess). What I think about is that a NN could
be trained to evaluate a position or some aspects of a position (e.g.
the structure of your pawns, how safe is the position of your king,...).

The idea behind is that evaluating a position is (as I see it) closely
related to pattern-recognition. An experienced human chess player is
able to identify a position as weak, even though there are no tactical
dangers to perceive and he/she has not seen the position before. So how
does he/she know? I think it is due to pattern recognition: He/She has
seen similiar positions before or has made the experience that a certain
pawn structure is a weak point. Once a chess master (I don't remember his
name) was asked how many moves he prefigures in advance and he answered
"normally: none". Even though this is an exageration, of course, his
answer shows that it is more important to be able to evaluate a position
than to prefigure a lot of moves! Interestingly todays chess programs
take the other way: the use brute force! And this is the reason why
computers are good in chess, but not in games like Go. In Go there are
more possibilities and brute force will not work so well. So what I
think is that we should put more emphasis on the evaluation of positions
(of a game) rather than on brute force. And maybe NNs could help !?

Any comments are appreciated!

Peter (bruhn@uxe.wu-wien.ac.at)
From: ajr@eng.cam.ac.uk (Tony Robinson)
Newsgroups: comp.ai.neural-nets
Subject: Re: Summary of CUPS + new question
Date: 10 Sep 92 09:05:01 GMT
Organization: Cambridge University Engineering Department, UK

In comp.ai.neural-nets Scott Fahlman <sef+@cs.cmu.edu> wrote:

> It's probably more correct to update
> a weight AFTER the error has propagated backwards across it, but if your
> weight-update steps are large enough for this to matter, you're going to
> get in trouble anyway due to stochastic fluctuations in the data.

Agreed that this should not matter much either way, but I think the original
poster, Jeff Berkowitz <jjb@sequent.com> has a point.

Doing the calculation of "deltas" first, then weight updates, gives the true
per-pattern gradient based on the original weights.  However, if you
calculate the deltas for the next layer downwards based on the new weights
higher up, perhaps you work a little in the new weight space.  I think that
this could result in a small speed up (I'd have to think a bit more to make
sure), but to do the process properly, you should calculate all the new
activations higher up, then recalculate new deltas for use with the new
weights.  This is O(n^2) in the number of layers, so isn't worthwhile.

I'm not a great fan of per-pattern update anyway (prefering updates on
something like the square root of the number of training patterns), but I
think this is an interesting idea.

Tony [Robinson]
Newsgroups: comp.ai.neural-nets
From: hovila@jyu.fi (Ari Hovila)
Subject: Adjusting weights in backprop simulators
Organization: University of Jyvaskyla, Finland
Date: Wed, 9 Sep 1992 09:47:35 GMT

In article 6499 of comp.ai.neural-nets jjb@sequent.com (Jeff Berkowitz)
writes:

(...some stuff deleted)
>Now, the question.  While I was trying to debug my backprop simulator,
>my wife discovered what appears to be a subtle difference between the
>precise description of backprop and several of the "C" implementations
>I've picked up via ftp.

>In the "original" paper (Rumelhart, Hinton, Williams, "Learning
>Internal Representations by Error Propogation", 1986) the backward
>pass is described as follows:

>	...The first step is to compute delta for each of
>	the output units. [...] We can then compute the
>	weight changes for all connections that feed into
>	the final layer.  AFTER this is done, then compute
>	deltas for all units in the penultimate layer...[etc,
>	emphasis mine.]
...
>as I read the description, I should change the weight FIRST as I back
>up, and then use the NEW value in the accumulated error.   At least
>the Dayhoff description pretty much states this in black and white.

Well, this has been bugging me too. I have impelented a bp-simulator
with C, and I use the method you suspected was 'an implementation
error' (i.e. calculate deltas first and then adjust the weights).

I'm really not sure what the main difference between these
two approaches is, but at least in a book by James Freeman (Neural
Networks, algorithms, applications and programming techniques) it
is very clearly stated that you should calculate deltas for hidden
layer nodes before adjusting the outgoing weights.

Since weight changes are usually small it could be possible that the
algorithm works both ways. I doubt that anyone has seen a case where
one of these methods works while the other one fails...

It would be nice to see other comments on this.

Ari Hovila,
University of Jyvaskyla, Finland
internet e-mail address: hovila@jyu.fi
From: rod@ctd.comsat.com
Newsgroups: comp.ai.neural-nets
Subject: Solicit Comments on Neural Network Software
Keywords: Neural Network
Date: 4 Sep 92 21:34:57 GMT
Organization: COMSAT Labs
NNTP-Posting-Host: bugs.ctd.comsat.com

My department is considering purchasing a neural network software package
to run an HP Series 700 workstation.  I have obtained literature on Neural Ware
and Math Works Neural Tool Box.  Would anyone be willing discuss their experience
in working with any of these software systems?  We are looking for a package
that is user friendly, yet flexible and could be easily expanded to support more
complex functions on an as needed basis.

-- 
		Roderick J. Ragland, Scientist
		e-mail:  rod@ctd.comsat.com
Newsgroups: comp.ai.neural-nets
From: kavuri@lips2.ecn.purdue.edu (Surya N Kavuri )
Subject: function approximation with neural nets : what is right ?
Organization: Purdue University Engineering Computer Network
Date: Thu, 10 Sep 1992 15:36:52 GMT

  
   In using a neural net to approximate a function, it is done with
   out any a priori information on what class of functions we are 
   looking for.  For example, if I know the function is some 
   polynomial, there is no way to impose this condition on the net.

   Is not this a disadvantage ?

            

   Surya Kavuri
From: palachar@bert.eecs.uic.edu (Prasad Palacharla)
Subject: C++ class library for NN
Summary: Can any one help me find out about existing C++ class libraries / software tools for implementing
Keywords: C++, NN
Organization: University of Illinois at Chicago
Date: Fri, 11 Sep 1992 19:21:42 GMT

Hello world:

I request you to point out where can I access / purchase any C++ class libraries or software tools for
implementing neural network models.

Thankyou for your help.

Prasad Palacharla,
Univ. of Illinois at Chicago.
(palachar@bert.eecs.uic.edu)
From: mcramm@dct.ac.uk
Newsgroups: comp.ai.neural-nets
Subject: Re: cerebellum networks
Date: 8 Sep 92 13:35:31 GMT
Organization: Dundee Institute of Technology

In article <IRH.92Sep7172029@leo.bby.oz.au>, irh@leo.bby.oz.au (Ian R Heddle) writes:
> 
> Does anyone out there heard of cerebellum networks? Apparently these
> networks were developed at the University of New Hampshire by a Dr
> Aldus. Any hint's or clues as to these networks would be appreciated.
> Regards,
> Ian Heddle.
> --
> ________________________________
>   Ian Heddle
>   Burdett, Buckeridge and Young
>   Melbourne, Australia
>   Internet irh@melba.bby.oz.au

Apologies for posting this - I can't reply for some reason!

I'm using J.S. Albus' CMAC (Cerebellar Model Artiuclation Controller [or
Arithemetic Computer - take your pick]) for my PhD project. If you
contact me ddirectly I might be able to supply some recent references.

(If there are enough requests I'll post to this group)

 Angus M. Marshall, (Research Student),
 Dept. of Mathematical & Computer Sciences, Dundee Institute of Technology,
 Bell St., Dundee DD1 1HG, Scotland                                  
 e-mail: MCRAMM@UK.AC.DCT.CC.VAXB           
         MCRAMM@UK.AC.DCT.CLUS                                 // 
         amarshal@uk.ac.dund.mcs                              //
 Phone: (+44 382) 308000 ext. 2265 or (+44 382) 308600    \\ //
                                                           \X/
".. and in the grove, by gate and hill, midst  merry  throng  and  market
clatter -  stood  the  Hall  of  the Mountain Grill, where table strain'd
'neath loaded platter "                             
(from the legend of Beenzon Toste - Hawkwind)
From: wagner@ike.ucdavis.edu (John Wagner)
Newsgroups: comp.ai.neural-nets
Subject: Re: Looking for Williams and Zipser
Date: 14 Sep 92 21:32:29 GMT
Reply-To: wagner@ike.ucdavis.edu
Organization: University of California, Davis

The ref you are looking for is
	Williams, Ronald J.; and Zipser, David: A learning
	algorithm for continually running fully recurrent
	neural networks.  Neural Computation, 1(2):270-280, 1989.

---
John Wagner
Institute of Theoretical Dynamics
University of California, Davis

Bush/Quayle 1992: Saddam has a job, do you?

My opinions aren't even mine, much less those of anyone else...
+------------------------------------+----------------------------+
| jmwagner@ucdavis.edu               | Wenn Du meinen Quelltext   |
| jmwagner@ucdavis                   | verstehen kannst, bist Du  |
| {ucbvax, lll-crg}!ucdavis!jmwagner | mir einen Schritt voraus!  |
+------------------------------------+----------------------------+
From: edwin@maui.cs.ucla.edu (Edwin Tisdale)
Newsgroups: comp.ai.neural-nets
Subject: Re: function approximation with neural nets : what is right ?
Date: 10 Sep 92 20:48:08 GMT
Organization: UCLA Computer Science Department

In article <1992Sep10.153652.12503@noose.ecn.purdue.edu> kavuri@lips2.ecn.purdue.edu (Surya N Kavuri ) writes:
>  
>   In using a neural net to approximate a function, it is done with
>   out any a priori information on what class of functions we are 
>   looking for.  For example, if I know the function is some 
>   polynomial, there is no way to impose this condition on the net.
>
If you **know** that the function you are trying to imitate is best
approximated by a polynomial then you **should** use a polynomial.
The polynomial is just another kind of network.  The non-linear
units are just the powers ( x^0, x^1, x^2, ...) of the input variable.
People sometimes call these things Sigma-Pi units.
The weights are just the coefficients (a_0, a_1, a_2, ...) which can
be estimated using conventional polynomial regression.
The problem is a little harder if you don't have much data to "train"
with and you don't know which (or even how many) terms (units) to
include in your network.  You might try using variable exponents (x^w).
But then you need a learning algorithm to propagate errors backward to
the exponents.  In short, you should always try to use the network
architecture which is most appropriate for your application.  But
unless you have some good reason for believing one is better than
another, any of the black-box models will do.  And you should just
try the one that is most convenient.  Hope this helps, Bob Tisdale
UCLA-CSD.
Newsgroups: comp.ai.neural-nets
From: volker@ecrc.de (Volker Kuechenhoff)
Subject: Re: Travelling salesman problem: help needed
Summary: References on TSP 
Reply-To: volker@ecrc.de
Organization: European Computer industry Research Centre GmbH.
Date: Mon, 7 Sep 1992 11:21:47 GMT


For the TSP one can distinguish two different approaches within NN:

1. Hopfiled/Tank : e.g. 
@article{Hopfield85biolcybern,
	author={J. Hopfield and D. Tank},
	title={{'Neural'} Computation of Decisions in Optimization Problems},
	journal={Biological Cybernetics},
	publisher=springer,
	volume=52,pages="141-152",
	year=1985}

2. Kohonen : e.g. 
@article{Angeniol88nn,
	author={B. Angeniol and G. La Croix Vaubois and J. Le Texier},
	title={Self-Organizing Feature Maps and the Traveling Salesman
Problem},
	journal={Neural Networks},
	volume=1,pages="289-293",
	year=1988}

I have tried to put an overview together that considers different new techniques
(also genetic algorithms and simulated annealing) for the TSP problem:

@techreport{Newsearch92,
	Author={Volker K{\"u}chenhoff},
	title={Novel Search Techniques - an Overview},
	Institution=ecrc,
	month=jan,year=1992},
	number={{IR-LP-91-19 - CHIC Deliverabele 5.3.3.1}}}


Cheers,
	Volker


---
Volker Kuechenhoff,
European Computer Industry Research Centre      Tel. + (49) 89-92699-155
Arabellastr 17, 8000 Muenchen 81. Germany.	FAX. + (49) 89-92699-170

e-mail   volker@ecrc.de   


Newsgroups: comp.ai.neural-nets