C++ Neural Networks and Fuzzy Logic: Preface

Using the Simulator to Calculate Error

You obtain the error for the test set by running the simulator in Training mode (you need to temporarily copy

the test data with expected outputs to the training.dat file) for one cycle with weights loaded from the weights

file. Since this is the last and only cycle, weights do not get modified, and you can get a reading of the average

error. Refer to Chapter 13 for more information on the simulator’s Test and Training modes. This approach

has been taken with five runs of the simulator for 500 cycles each. Table 14.5 summarizes the results along

with the parameters used. The error gets better and better with each run up to run # 4. For run #5, the training

set error decreases, but the test set error increases, indicating the onset of memorization. Run # 4 is used for

the final network results, showing test set RMS error of 13.9% and training set error of 6.9%.

Table 14.5 Results of Training the Backpropagation Simulator for Predicting the Percentage Change in the

S&P 500 Index