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

I want to do classification using ANN ‘patternnent’ function. I am new to ANN. I have already normalised data and also divided into train and test dataset. Further, I want to divide train data into three equal parts and perform cross-validation technique, later I want to apply the best model on test dataset to calculate the accuracy. I am unable to understand how to use the ‘patternnet’ and other functions and complete the task the way I want. Could you please help me out? Thank you.

ANSWER

The optimal approach is to learn the best way to take advantage of existing MATLAB functions and structure. Although it is not necessarily the approach that you proposed, it is no less valid.

For example, by default, MATLAB NN design functions automatically

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….

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