Is it possible to connect nodes in one layer to a specific node in the next layer using the Matlab neural net toolbox?

Hi,

I'm completely new to Matlab and I'm finding it difficult to find any information on this particular problem. I'm trying to create a probability neural network. Im using newpnn and modifying it for my own needs. As it stands I have 20 nodes in my pattern layer. I want to connect 10 to one node and 10 to the other in the next layer (the summation layer). Is this possible? I have attached a diagram to show what I mean.

Also how would I perform kernel density estimation in the summation nodes?

Thanks,

Steve

Answer 1

[Greg Heath]

The PNN has a single hidden layer. The output layer AUTOMATICALLY performs the summation.

Why would you want to make the net more complicated by adding a separate summation layer?

The only thing you have to do is

1. Make sure your target matrix only contains columns from eye(2).

2. Divide the data into trn/val/tst subsets.

3. Standardize the training data and use the mean and stdv vectors to normalize the val and test data.

4.Initialize the best val subset error rate bestspread = 0, bestvalerr = inf

5. Loop over positive spread values surrounding the default of 1 (e.g., 0.1:0.1:2)

    a.Use the training data to create a net
    b. Obtain and store the train, val and test subset error rates vs spread
    c. Compare the val error rate with the best. If is lower, it becomes the best.

6. The test set error rate corresponding to the minimum val set error is the unbiased estimate of error on unseen non-training data.

7. If performance is unsatisfactory consider a double loop design where the outer loop creates new trn/val/tst data divisions.

8. If performance is still unsatisfactory, you may have to increase the size of your training subset.

Hope this helps.

Thank you for formally accepting my answer

Greg

[Greg Heath]

Mea culpa!

After you have a design, you can probably remove all connections between class 1 hidden nodes and the class 2 output node and vice versa.

Interesting.