How not to normalize the data in patternnet function and how to do K-fold cross validation technique?

John Michell answered . 2025-07-09 21:39:00

 1. Normalize input and target data   

 2. Divide the data into training, validation and test subsets.

 3. Initialize default parameter settings including random initial 
    weights

 4. Train the net until one of several conditions occur. For example
    a. Training subset error is reduced to a specified level
    b. Validation subset error increases continually for a 
       specified number (default = 6) of epochs
    c. A maximum number (default = 1000) of epochs is reached.

5. Use the target normalization parameters to 
unnormalize the output data

6. Calculate the performance measure

Simple examples are given in the help and doc documentation examples obtained using the commands

 help patternnet

and

 doc patternnet

Typically, there are two main reasons for unsuccessful efforts:

 1. Inadequate (high or low) specification for the number of hidden nodes

 2. Inappropriate choice of random initial weights.

Consequently, I have devised a double loop approach where

 1. The outer loop is over an interval for the number of hidden nodes

 2. The inner loop is over a number of random initial weight settings

Examples can be found by searching both NEWSGROUP and ANSWERS using search words that include

GREG PATTERNNET

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