Direct Inverse Control of Sensors by Neural Networks for Static/Low Frequency Applications

Abstract

This paper addresses the issue of direct inverse control for two types of nonlinear transducer systems characterised by: piecewise linear input-output transfer function; hysteresis occurring in the input-output transfer function; with the aim of establishing whether some relationship exists between the severity of different nonlinearities and the complexity of the network required to control such nonlinearities in static/low-frequency sensor applications. The compensation is performed using an artificial neural networks approach. The networks chosen were a static MLP and if tap-delayed line MLP, both trained by an improved BKP method which included a form of dynamic learning management.