mirage

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

DSpace/Manakin Repository

 

Search DR-NTU


Advanced Search Subject Search

Browse

My Account

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

Show full item record

Title: Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences
Author: Patra, Jagdish Chandra; Chakraborty, Goutam; Meher, Pramod Kumar
Copyright year: 2008
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.
Subject: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Type: Journal Article
Series/ Journal Title: IEEE transactions on circuits and systems I: regular papers
School: School of Computer Engineering
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].
Version: Accepted version

Files in this item

Files Size Format View
37.Neural-Netwo ... nearization (Reviewed).pdf 1.209Mb PDF View/Open
   

DOI Query

- Get published version (via Digital Object Identifier)
   

This item appears in the following Collection(s)

Show full item record

Statistics

Total views

All Items Views
Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences 389

Total downloads

All Bitstreams Views
37.Neural-Network-Based Robust Linearization (Reviewed).pdf 215
2008IEEE_TCAS_Goutam_Sensor.pdf 57

Top country downloads

Country Code Views
United States of America 94
Singapore 52
India 43
China 18
United Kingdom 13

Top city downloads

city Views
Mountain View 72
Singapore 52
Beijing 14
New Delhi 10
Derby 7

Downloads / month

  2014-05 2014-06 2014-07 total
37.Neural-Network-Based Robust Linearization (Reviewed).pdf 0 0 18 18