Topology Selection for Signal Change Detection in Sensor Networks: RBF vs MLP

This paper documents the results of experimental simulations designed to compare the performance of multilayer perceptron (MLP) and radial basis function (RBF) based sensor signal change detection systems. The two systems are simultaneously executed in parallel on the same input signals. Both systems share an identical implementation with the exception of the activation function used in the hidden layers of the artificial neural networks. Previous experiments have employed only Multilayer Perceptrons with sigmoidal activation functions. The results of these experiments quantitatively show the advantages and disadvantages of Radial Basis neural activation for both the function prediction and function correlation neural networks tested.