Low Cost Neural Network Hardware for Control

Feedforward artificial neural networks are universal function approximators and inherently parallel computing structures. Because of the lack of appropriate hardware realisations, applications of neural networks are predominantly implemented as sequential programs on digital processors. In this paper we describe an analogue integrated circuit realisation of a local response neural network (LCNN) that achieves a high degree of parallel computation in a small size, low cost and low power consumption. Because it can directly receive analog inputs from sensors and output analog control signals to actuators it is well suited as a building block for real-time control systems.