C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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The backprop.cpp file implements the simulator controls. First, data is accepted from the user for network

parameters. Assuming Training mode is used, the training file is opened and data is read from the file to fill

the IO buffer. Then the main loop is executed where the network processes pattern by pattern to complete a

cycle, which is one pass through the entire training data set. (The IO buffer is refilled as required during this

process.) After executing one cycle, the file pointer is reset to the beginning of the file and another cycle

begins. The simulator continues with cycles until one of the two fundamental criteria is met:

1.  The maximum cycle count specified by the user is exceeded.

2.  The average error per pattern for the latest cycle is less than the error tolerance specified by the

user.

When either of these occurs, the simulator stops and reports out the error achieved, and saves weights in the

weights.dat file and one output vector in the output.dat file.

In Test mode, exactly one cycle is processed by the network and outputs are written to the output.dat file. At

the beginning of the simulation in Test mode, the network is set up with weights from the weights.dat file. To

simplify the program, the user is requested to enter the number of layers and size of layers, although you

could have the program figure this out from the weights file.