<title>Realizing the potential of neural network implementation technologies: methods of using large neural networks</title>

Abstract

In the longer term amorphous silicon and other technologies are promising to allow the implementation of large neural networks directly in hardware. However, although a number of application areas, such as telecommunication network management and image recognition, could benefit from such large artificial neural networks because they require large numbers of inputs and/or outputs, it is currently difficult to determine the benefits of large networks in such application areas for the following main reasons. Firstly, design application engineers do not yet have such tool available to them; secondly, and more importantly, many popular neural network training algorithms do not scale well. This paper suggests new methods of combining many small networks to produce a large composite system capable of extending the range of problems to which neural network techniques can be applied. It shows how large networks can be used to extend the ideas of fuzzy logic so that non-linear dependences between vectors can be dealt with.