C++ Neural Networks and Fuzzy Logic: Preface

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training.dat This file contains the input patterns for training. You can have as large a file as

you’d like without degrading the performance of the simulator. The simulator caches data in memory

for processing. This is to improve the speed of the simulation since disk accesses are expensive in

time. A data buffer, which has a maximum size specified in a #define statement in the program, is

filled with data from the training.dat file whenever data is needed. The format for the training.dat file

has been shown in the Training mode section.

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test.dat The test.dat file is just like the training.dat file but without expected outputs. You use this

file with a trained neural network in Test mode to see what responses you get for untrained data.

•

output.dat The output.dat file contains the results of the simulation. In Test mode, the input and

output vectors are shown for all pattern vectors. In the Simulator mode, the expected output is also

shown, but only the last vector in the training set is presented, since the training set is usually quite

large.

Shown here is an example of an output file in Training mode:

for input vector:

0.400000  −0.400000

output vector is:

0.880095

expected output vector is:

0.900000

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C++ Neural Networks and Fuzzy Logic:Preface

C++ Implementation of a Backpropagation Simulator

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