Compensation of nonlinear smart sensors is an important topic that must always be considered to assure the accuracy of measurement systems. Nowadays, with the advent of microprocessor devices in smart sensors, advance...
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Compensation of nonlinear smart sensors is an important topic that must always be considered to assure the accuracy of measurement systems. Nowadays, with the advent of microprocessor devices in smart sensors, advanced compensation algorithms can be implemented to improve the accuracy of measurement. In this paper, an inverse modeling methodology based on B-spline is proposed for the compensation of nonlinear smart sensors. To avoid complicated least squares solution of the B-spline, a training algorithm in a recursive form is proposed to reduce the training cost and make the on-chip training of B-spline available. Moreover, the choices of B-spline knots and training points are important designed parameters in this methodology. So the free knot insertion algorithm and training points' selection method are used prior to the training process to improve the accuracy of the inverse models and avoid the under and over fitting. Simulations and results are presented to validate the theoretical expectations. (C) 2011 Elsevier Ltd. All rights reserved.
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