C++ Neural Networks and Fuzzy Logic: Preface

Run#ToleranceBetaAlphaNFmax cyclescycles runtraining set errortest set error

10.0010.50.0010.00055005000.1509380.25429

20.0010.40.0010.00055005000.1149480.185828

30.0010.3005005000.09364220.148541

40.0010.2005005000.0689760.139230

50.0010.1005005000.06214120.143430

NOTE:  If you find the test set error does not decrease much, whereas the training set error

continues to make substantial progress, then this means that memorization is starting to set in

(run#5 in example). It is important to monitor the test set(s) that you are using while you are

training to make sure that good, generalized learning is occurring versus memorizing or

overfitting the data. In the case shown, the test set error continued to improve until run#5,

where the test set error degraded. You need to revisit the 12−step process to forecasting

model design to make any further improvements beyond what was achieved.

C++ Neural Networks and Fuzzy Logic:Preface

Storing Data in Different Files

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To see the exact correlation, you can copy any period you’d like, with the expected value output fields

deleted, to the test.dat file. Then you run the simulator in Test mode and get the output value from the

simulator for each input vector. You can then compare this with the expected value in your training set or test

set.

Now that you’re done, you need to un−normalize the data back to get the answer in terms of the change in the

S&P 500 index. What you’ve accomplished is a way in which you can get data from a financial newspaper,

like Barron’s or Investor’s Business Daily, and feed the current week’s data into your trained neural network

to get a prediction of what the S&P 500 index is likely to do ten weeks from now.

Here are the steps to un−normalize: