ABSTRACT
This chapter provides an introduction to computational neural networks (CNNs), which are parallel distributed information structures that can be used to carry out pattern classification, clustering, function approximation and optimisation. An overview is presented of how CNNs function including a description of the network processing elements (PEs) and the different network topologies and how CNNs learn. A classification of CNNs into different types is then provided followed by a discussion of the advantages of these tools and their application domains. The chapter concludes with two examples to demonstrate their use in two diverse areas: one on using CNNs to model interregional telecommunication traffic flows in Austria and the other on comparing three neural classifiers of Landsat imagery for Vienna.
