Feedforward neural networks for compound signals

Abstract

In this paper we consider possible extensions of the classical multilayer artificial neural network model to the situation when the signals processed by the network are by definition compound and possibly structured. We discuss existing approaches to this problem in various contexts and provide our own model–the Normalizing Neural Network–for networks that process vectors as signals. We discuss possible uses of the proposed approach in a series of case studies.