ADAPTIVE K-MEANS CLUSTERING ALGORITHM
RADIAL BASIS FUNCTION (RBF) NETWORKS
Using a clustering procedure (K-means batch or adaptive) creates a set of cluster centers , which can be thought of as the average input vector for the kth cluster, or more appropriately, as the prototype vector for that cluster.
Hence from the basis function , we see that the output only has a significant value if the Euclidean distance between the input vector and a prototype vector is within a radius 2 around .
For the RBF network, we shall use only two neurons in the hidden layer.
RECALL: MLP NEURAL NETWORK
For an RBF network, the activation function of the output neurons is linear i.e.
BACK TO THE EXAMPLE FROM PREVIOUS LECTURES
In the previous lecture, we considered the use of two hidden neurons in the RBF network as shown below.
Reading Paper :
Growing RBF structures using self-organizing maps