ANN bypassing hidden layer for an input

Question

I have just been set an assignment to calculate some ANN outputs and write an ANN. Simple stuff, done it before, so I don't need any help with general ANN stuff. However, there is something that is puzzling me. In the assignment, the topology is as follows(wont upload the diagram as it is his intellectual property):-

• 2 layers, 3 hiddens and one output.
• Input x1 goes to 2 hidden nodes and the output node.
• Input x2 goes to 2 hidden nodes.

The problem is the ever so usual XOR. He has NOT mentioned anything about this kind of topology before, and I have definitely attended each lecture and listened intently. I am a good student like that :)

I don't think this counts as homework as I need no help with the actual tasks in hand.

Any insight as to why one would use a network with a topology like this would be brilliant.

Regards

Answer 1

Does the neural net look like the above picture? It looks like a common XOR topology with one hidden layer and a bias neuron. The bias neuron basically helps you shift the values of the activation function to the left or the right.

For more information on the role of the bias neuron, take a look at the following answers:

• Role of Bias in Neural Networks
• XOR problem solvable with 2x2x1 neural network without bias?
• Why is a bias neuron necessary for a backpropagating neural network that recognizes the XOR operator?

Update

I was able to find some literature about this. Apparently it is possible for an input to skip the hidden layer and go to the output layer. This is called a skip layer and is used to model traditional linear regression in a neural network. This page from the book Neural Network Modeling Using Sas Enterprise Miner describes the concept. This page from the same book goes into a little more detail about the concept as well.