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Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences

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Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences

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dc.contributor.author Patra, Jagdish Chandra
dc.contributor.author Chakraborty, Goutam
dc.contributor.author Meher, Pramod Kumar
dc.date.accessioned 2011-09-29T06:29:02Z
dc.date.available 2011-09-29T06:29:02Z
dc.date.copyright 2008
dc.date.issued 2011-09-29
dc.identifier.citation Patra, J. C., Chakraborty, G., & Meher, P. K. (2008). Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(5), 1316-1327.
dc.identifier.issn 1549-8328
dc.identifier.uri http://hdl.handle.net/10220/7122
dc.description.abstract A novel artificial neural network (NN)-based technique is proposed for enabling smart sensors to operate in harsh environments. The NN-based sensor model automatically linearizes and compensates for the adverse effects arising due to nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental variables. To show the potential of the proposed NN-based technique, we have provided results of a smart capacitive pressure sensor (CPS) operating under a wide range of temperature variation. A multilayer perceptron is utilized to transfer the nonlinear CPS characteristics at any operating temperature to a linearized response characteristics. Through extensive simulated experiments, we have shown that the NN-based CPS model can provide pressure readout with a maximum full-scale error of only 1.5% over a temperature range of 50 to 200 with excellent linearized response for all the three forms of nonlinear dependencies considered. Performance of the proposed technique is compared with a recently proposed computationally efficient NN-based extreme learning machine. The proposed multilayer perceptron based model is tested by using experimentally measured real sensor data, and found to have satisfactory performance.
dc.format.extent 12 p.
dc.language.iso en
dc.relation.ispartofseries IEEE transactions on circuits and systems I: regular papers
dc.rights © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI: http://dx.doi.org/10.1109/TCSI.2008.916617].
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
dc.title Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.doi http://dx.doi.org/10.1109/TCSI.2008.916617
dc.description.version Accepted version
dc.identifier.rims 128670

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