No subject

The file nascimento.phd.tarZ is now available for copying from
the anonymous ftp-site 'slarti.csc.umist.ac.uk' (130.88.116.3):

Author: Cairo L. Nascimento Jr.  (cairo at csc.umist.ac.uk)
PhD thesis title: Artificial Neural Networks in Control and Optimization
Submission date: February 1994
Supervisor: Dr. Martin B. Zarrop (zarrop at csc.umist.ac.uk)
            UMIST - Control Systems Centre
            P.O. Box 88 - Sackville Street
            Manchester M60 1QD
            United Kingdom


Abstract:

       This thesis concerns the application of artificial neural networks to
solve optimization and dynamical control problems.
       A general framework for artificial neural networks models is introduced
first. Then the main feedforward and feedback models are presented. The IAC
(Interactive Activation and Competition) feedback network is analysed in detail.
It is shown that the IAC network, like the Hopfield network, can be used to solve
quadratic optimization problems.
       A method that speeds up the training of feedforward artificial neural
networks by constraining the location of the decision surfaces defined by the
weights arriving at the hidden units is developed.
       The problem of training artificial neural networks to be fault tolerant
to loss of hidden units is mathematically analysed. It is shown that by considering
the network fault tolerance the above problem is regularized, that is the number of
local minima is reduced. It is also shown that in some cases there is a unique set
of weights that minimizes a cost function. The BPS algorithm, a network training
algorithm that switches the hidden units on and off, is developed and it is shown
that its use results in fault tolerant neural networks.
       A novel non-standard artificial neural network model is then proposed to
solve the extremum control problem for static systems that have an asymmetric
performance index. An algorithm to train such a network is developed and it is shown
that the proposed network structure can also be applied to the multi-input case.
       A control structure that integrates feedback control and a feedforward artificial
neural network to perform nonlinear control is proposed. It is shown that such a
structure performs closed-loop identification of the inverse dynamical system.
The technique of adapting the gains of the feedback controller during training is
then introduced. Finally it is shown that the BPS algorithm can also be used in
this case to increase the fault tolerance of the neural controller in relation to
loss of hidden units.
       Computer simulations are used throughout to illustrate the results.

-----------------------------------------------------------------------------------

The thesis is 226 pages (17 preamble + 209 text). Hardcopies are not available
at the moment.

To obtain a copy of the Postscript files:

 % ftp slarti.csc.umist.ac.uk
 > Name: anonymous
 > Password: <Your email address>
 > cd /pub/neural/cairo
 > binary
 > get nascimento.phd.tarZ
 > quit

The file nascimento.phd.tarZ is a unix TAR file which contains the following
postscript files (compressed by the standard unix command "compress"):

    File               Size in bytes
nascimento.phd.tarZ       2015232
chap01.ps.Z                 36737
chap24.ps.Z               1041029
chap58.ps.Z                928199

When uncompressed the file sizes and number of pages in each file are:
  File          Size in bytes     Number of pages
chap01.ps          109471              22
chap24.ps         3315662              97
chap58.ps         2913551             107
                 ---------           -----
                  6338684             216

To obtain one of the postscript files from the TAR file, use:

  % tar tvf nascimento.phd.tarZ
        (list the table of contents of the TAR file)
  % tar xvf nascimento.phd.tarZ chap24.ps.Z
        (extracts only the file chap24.ps.Z from the TAR file)
  % uncompress -v chap24.ps.Z
  % lpr -s -P<printer-name> chap24.ps
        (do not delete or compress the PS file until the printing is finished)


OBS: 

1) The uncompressed postscript files can be viewed using "ghostview" (or "ghostscript"),
but I don't know about "pageview".

2) If you have GZIP installed locally, consider compressing the PS files using it.
Using the command "gzip -9v filename" the size of the compressed PS files will be
respectively 24568, 613052, 637269 bytes (total using GZIP -9: 1274889 bytes,
total using COMPRESS: 2005965 bytes; 1274889 / 2005965 = 63.6 %).

3) Some of my other publications are available in the same directory. For more
details get the file /pub/neural/cairo/INDEX.TXT.

 
------------------------------------------------------------------------- 
Cairo L. Nascimento Jr.         | E-Mail: cairo at csc.umist.ac.uk           
 UMIST - Control Systems Centre | Tel: +(44)(61) 200-4659, Room C70       
 P.O. Box 88 - Sackville Street |   or +(44)(61) 236-3311, Ext.2821       
 Manchester M60 1QD             | Tel. Home: +(44)(61) 343-3979           
 United Kingdom                 | WHOIS handle for ds.internic.net: CLN2  
-------------------------------------------------------------------------

After 1st June 1994 my surface address will be:

              Cairo L. Nascimento Jr.
              Instituto Tecnologico de Aeronautica,
              CTA - ITA - IEE- IEEE
  12228-900 - Sao Jose' dos Campos - SP
              Brazil

E-mail in Brazil (after 1st June 1994): ita at fpsp.fapesp.br
                 (please, include my name in the subject line).
The email address cairo at csc.umist.ac.uk should remain operational for some months
after June/94.

....................................................................................
END