Accurate neural model identification of measurement devices

Abstract

A neural approach to modeling measurement devices is presented. This approach allows the usual components of a measurement apparatus (transducers, filters, amplifiers, analog-to-digital converters, etc.) to be easily modeled by means of suitably trained Artificial Neural Networks. Two applications regarding analog and mixed analog/digital devices are reported, highlighting the peculiarity of this approach and the accuracy obtainable.