C++ Neural Networks and Fuzzy Logic: Preface

•  The mode (select 1 for training)

•  The values for the error tolerance and the learning rate parameter, lambda or beta

•  The maximum number of cycles, or passes through the training data you’d like to try

•  The number of layers (between three and five, three implies one hidden layer, while five implies

three hidden layers)

•  The size for each layer, from the input to the output

The simulator then begins training and reports the current cycle number and the average error for each cycle.

You should watch the error to see that it is on the whole decreasing with time. If it is not, you should restart

the simulation, because this will start with a brand new set of random weights and give you another, possibly

better, solution. Note that there will be legitimate periods where the error may increase for some time. Once

the simulation is done you will see information about the number of cycles and patterns used, and the total

and average error that resulted. The weights are saved in the weights.dat file. You can rename this file to use

this particular state of the network later. You can infer the size and number of layers from the information in

this file, as will be shown in the next section for the weights.dat file format. You can have a peek at the

output.dat file to see the kind of training result you have achieved. To get a full−blown accounting of each

pattern and the match to that pattern, copy the training file to the test file and delete the output information

from it. You can then run Test mode to get a full list of all the input stimuli and responses in the output.dat

file.