readme

几个应用程序的Ｃ语言源代码

			    NEURAL NETWORK PC TOOLS

			     SOFTWARE USER'S GUIDE

     $Revision:   1.0  $            $Date:   18 Sep 1989  9:38:14  $

     INTRODUCTION

     The software described in this User's Guide is that described in the
     chapter on Neural Network PC Tool Implementations in the book entitled
     Neural Network PC Tools: A Practical Guide, to be published by
     Academic Press in 1990.  This software may be copied and distributed
     AS LONG AS IT IS NOT MODIFIED.  In particular, any problems with the
     source code should be brought to the attention of the authors.

     If you use this software, consider it as shareware and please send
     $5.00 to the authors at the following address:  Roy Dobbins, 5833
     Humblebee Road, Columbia, MD 21045.  As additions are made to this
     software diskette, such as including self-organizing (Kohonen)
     networks, the price will increase.  It is anticipated that the price
     for the diskette sold in conjunction with the book will be about $20.

     BACKGROUND

     Much excitement exists due to the apparent ability of artificial
     neural networks to imitate the brain's ability to make decisions and
     draw conclusions when presented with complex, noisy and/or partial
     information.  This software is for the engineer or programmer who is
     interested in solving practical problems with neural networks.

     It is a myth that the only way to achieve results with neural networks
     is with a million dollars, a supercomputer, and an interdisciplinary
     team of Nobel laureates.  There are some commercial vendors out there
     who would like you to believe that, though.

     Using simple hardware and software tools, it is possible to solve
     practical problems that are otherwise impossible or impractical.
     Neural network tools (NNT's) offer a solution to some problems that
     can't be solved any other way known to the authors.

     THE BACK-PROPAGATION NNT: BATCHNET

     This release contains both source and executable code for a "standard"
     three layer back-propagation neural network.  The executable program
     is called batchnet.exe; its source code is in the file batchnet.c.
     The program for generating random weights used as input to the
     training run is weights.exe; its source code is in weights.c.  These
     files were compiled using Turbo C v 2.0, but can also be compiled in
     Microsoft C.

     They were compiled using the 80x87 emulator mode, so that they will
     run even if you don't have a co-processor.  If you have a coprocessor
     and want batchnet to run faster, which may be especially important in
     training, you can recompile batchnet.c using the 80x87 option. Always
     use the compact model.


     To run the batchnet program, you must specify the run file that it
     will use.	Look at the demo.bat and demo.run files to see what we
     mean.  Demo.bat also illustrates one of the options for batchnet; you
     can specify the interval of iterations between error printout.  (The
     error is the mean sum-squared error of the output nodes.)

     The other option for batchnet is to specify what sum-squared error is
     required for the program to terminate training.  The default value is
     0.04.  The default number of iterations between error printouts is
     100.

     In the run file, you specify a number of things.  Look at demo.run in
     detail to see what they are; there is explanation following the run
     data for the two runs that tell what goes where.

     First, you specify the number of runs.  The demo has two.	This is
     fairly typical.  You often have a training run followed by a test run,
     as is the case in the demo.

     You then specify the filenames for a number of files: the output file
     that gives the values of the output nodes for each pattern on the last
     iteration (or the only iteration, if you are in testing mode and there
     is only one iteration), the error file that gives you the average sum
     squared error value each specified number of iterations, the source
     pattern file (values normalized between 0 and 1), the input weights
     file (generated by weights.exe for a training run, and consisting of
     the output weights file from training for a testing run), and the
     output weights file which gives you weight values after the last
     iteration.

     Note that the pattern files have values for each input node followed
     by values for each output node followed by an ID field that you can
     use to identify each pattern in some way.	The input and output node
     values should be between 0 and 1.

     Following filenames, you specify, for each run, the number of input
     patterns, the number of epochs (iterations of entire pattern set), the
     number of input nodes, number of hidden nodes, number of output nodes,
     the value for the learning coefficient eta, and the value for the
     momentum factor alpha.  The number of epochs varies a lot during
     training, but often is in the range of 100-1000; during testing, you
     only do one iteration.

     Sample files are given that you can run with demo.bat; the output
     files you will get when you run the demo are already on the diskette
     as mytest.out, mytrain.out, mytrain.wts, mytest.wts, mytrain.err, and
     mytest.err.  You will get similar files without the "my" prefix when
     you run the demo.bat program, and you can compare corresponding files
     to see that they are the same.

     All you have to do is run "demo.bat" in order to both train and test
     the batchnet artificial neural network on the patterns in the
     train.pat and test.pat files.  These pattern files are built from
     actual electroencephalogram (EEG) spike parameter data, and illustrate
     the use of a parameter-based NNT.

     The training phase of the demo.bat will probably take about 45 minutes
     on a 4.77 MHz 8088 PC with coprocessor. A 10 MHz Grid 80286 Laptop with
     no coprocessor takes about 140 minutes. The coprocessor makes the
     difference!