Virtual compensator for correcting the disturbing variable effect in transducers

Abstract Transducers are exposed to different types of environments during the course of measurement. The performance of a transducer is affected adversely by variations in excitation quantities and change in ambient conditions including temperature and humidity. The effect of change in environmental conditions on the transducer and subsequent on its signal conditioning circuit is quite nonlinear in nature. Especially, change in transducer excitation causes its response characteristics highly nonlinear and complex signal processing is required invariably to obtain correct readout. In this paper, we propose an artificial neural network-based novel virtual compensator for correcting the effect of a disturbing variable in transducers. The correction is carried out by a nonlinear two-dimensional artificial neural network-based inverse model of the transducer trained with Levenberg–Marquardt learning algorithm. By training the neural model suitably, the digital readout of the applied input (measurand) is obtained which is independent of disturbing variable.

[1]  Jagdish C. Patra.  An artificial neural network-based smart capacitive pressure sensor , 1997 .

[2]  Adriaan van den Bos,et al.  Modeling and development of an ANN-based smart pressure sensor in a dynamic environment , 1999 .

[3]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[4]  J. A. Brandão Faria,et al.  Automated characterization of a bifurcated optical fiber bundle displacement sensor taking into account reflector tilting perturbation effects , 2000 .

[5]  Adriaan van den Bos,et al.  An ANN-based smart capacitive pressure sensor in dynamic environment , 2000 .

[6]  Ganapati Panda,et al.  An intelligent pressure sensor using neural networks , 2000, IEEE Trans. Instrum. Meas..

[7]  J. B. Gomm.  Adaptive neural network approach to on-line learning for process fault diagnosis , 1998 .

[8]  Pasquale Daponte,et al.  ANN-based error reduction for experimentally modeled sensors , 2002, IEEE Trans. Instrum. Meas..

[9]  O. Postolache,et al.  Neural networks in automated measurement systems: state of the art and new research trends , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[10]  Octavian Postolache,et al.  Minimizing temperature drift errors of conditioning circuits using artificial neural networks , 1998, IEEE Trans. Instrum. Meas..

[11]  Ganapati Panda,et al.  Nonlinear channel equalization for QAM signal constellation using artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Ganapati Panda,et al.  ANN-based intelligent pressure sensor in noisy environment , 1998 .