By Topic

Artificial neural network-based nonlinearity estimation of pressure sensors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
J. C. Patra ; Dept. of Electron. & Electr. Commun. Eng., Indian Inst. of Technol., Kharagpur, India ; G. Panda ; R. Baliarsingh

A new approach to pressure sensor modeling based on a simple functional link artificial neural network (FLANN) is proposed. The response of the sensor is expressed in terms of its input by a power series. In the direct modeling, using a FLANN trained by a simple neural algorithm, the unknown coefficients of the power series are estimated accurately. The FLANN-based inverse model of the sensor can estimate the applied pressure accurately. The maximum error between the measured and estimated values is found to be only ±2%. The existing techniques utilize ROM or nonlinear schemes for linearization of the sensor response. However, the proposed inverse model approach automatically compensates the effect of the associated nonlinearity to estimate the applied pressure. Frequent modification of the ROM or nonlinear coding data is required for correct readout during changing environmental conditions. Under such conditions, in the proposed technique, for correct readout, the FLANN is to be retrained and a new set of coefficients is entered into the plug-in module. Thus this modeling technique provides greater flexibility and accuracy in a changing environment

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:43 ,  Issue: 6 )