## Radial basis function network

A Radial Basis Function (RBF) neural network has input layer, hidden layer and output layer. The neurons in the hidden layer contain Gaussian transfer function, and its output is a linear combination of radial basis functions.
They are used in function approximation, time series prediction, and control. [http://en.wikipedia.org/wiki/Radial_basis_function_network]

To create and train RBF neural network with easyNeurons do the following:

1. Choose RBF architecture (in main menu choose Networks>RBF)
2. Enter architecture specific parameters (number of neurons in input layer)
3. Create training set (in main menu choose Training >New Training Set)
4. Train network
5. Test network

Step 1. To create RBF network, in main menu click Networks > RBF

Step 2. Enter number of neurons in input layer, and click Create button.

This will create the RBF neural network with two neurons in input layer, three neurons in rbf layer and one neuron in output layer.

Now we shall train this simple network, to learn from data. First we have to create the training set

Step 3. In main menu click Training > New Training Set to open training set wizard.

Step 4. Train network

TODO

Step 5. Test network

TODO