A new proposal for implementation of competitive neural networks in analog hardware

Abstract

This paper proposes the basic structure of a competitive neural network (CNN), based on analog IC techniques, for hardware implementation. This leads to a more compact project and allows real time processing. It is shown that the fundamental equations that describe the behavior of competitive neural networks possess a relationship with some basic electronic components. This fact allows the direct implementation of CNN with these electronic components. Initially the behavior of the fundamental equations of this type of neural networks is studied by means of software simulations. This behavior is then compared with the one obtained through electric simulations of the equivalent circuits originated from these fundamental equations. Simulations show that the most important features of the CNN are obtained with the presented implementation. Finally, a typical application is presented in the area of pattern clustering using synaptic weights to demonstrate an implementation using the techniques described.