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What are Radial Basis Function Networks (RBFNs) ?
A radial basis function network is a type of artificial neural network in which the activation functions are radial basis functions. The network's output is a linear combination of the inputs' radial basis functions and neuron parameters. Radial Basis Function Networks (RBFNs) are used for many uses: Function approximation, time series prediction, classification, and system control.
Architecture
Radial Basis Function Networks (RBFNs) have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer.
The input can be modelled as a vector of rea number and The output of the network is then a scalar function of the input vector
where N is the hidden layer's number of neurons, Ci is the centre vector for neuron I and ai is the linear output neuron's weight for neuron i. Radially symmetric about a central vector, functions that depend only on distance from that vector are called radial basis functions. All inputs are connected to each hidden neuron in the basic form.
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