Title :
Accurate neural model identification of measurement devices
Author :
Bernieri, Andrea ; Daponte, Pasquale ; Grimaldi, Domenico
Author_Institution :
Dipartimento di Ingegneria Ind., Univ. di Cassino, Italy
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
Keywords :
analogue-digital conversion; backpropagation; digital-analogue conversion; feedforward neural nets; filters; identification; instrumentation amplifiers; instruments; measurement theory; modelling; transducers; transfer functions; accurate neural model identification; amplifiers; analog devices; analog-to-digital converters; error model; feedforward network; filters; learning algorithms; measurement devices modelling; mixed analog/digital devices; node transfer function; nonlinearities; trained ANN; transducers; Analog-digital conversion; Artificial neural networks; Digital filters; IEEE members; Iterative algorithms; Particle measurements; Sensor phenomena and characterization; Sensor systems; Transducers; Transfer functions;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE
Conference_Location :
Brussels
Print_ISBN :
0-7803-3312-8
DOI :
10.1109/IMTC.1996.507315
