Optimal fusion operator selection: a neural-network-technique-based approach

Abstract

In this paper, we present a neural network based method that allows the optimal selection of a data fusion policy. We build dynamically the internal layer of a functional link network (FLN), we add to the classical FLN, a pruning algorithm, that allows to find the optimal architecture of the FLN and to define an optimal fusion policy. In order to use the FLN as a universal fusion operator, the functional expansion performed by its internal layer includes fusion operators. As the FLN minimize the mean square error (MSE) during the learning step, an optimal fusion policy is reached in the sense of the MSE. Some academic simulations validate our approach.