Browse DORAS
Browse Theses
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Lau, King-Tong and Guo, Weimin and Kiernan, Breda M. and Slater, Conor and Diamond, Dermot (2009) Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization. Sensors and Actuators B: Chemical, 1 (2). pp. 242-247. ISSN 0925-4005

Full text available as:

[img]PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work. Infrared sensor with a measuring range of 0~5% was used to measure carbon dioxide gas concentration within the range 0~15000 ppm. Neural networks were employed to fulfill the nonlinear output of the sensor. The Bayesian strategy was used to regularize the training of the back propagation neural network with a Levenberg-Marquardt (LM) algorithm. By Bayesian regularization (BR), the design of the network was adaptively achieved according to the complexity of the application. Levenberg-Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method. The results showed that the Bayesian regulating neural network was a powerful tool for dealing with the infrared gas sensor which has a large non-linear measuring range and provide precise determination of carbon dioxide gas concentration. In this example, the optimal architecture of the network was one neuron in the input and output layer and two neurons in the hidden layer. The network model gave a relationship coefficient of 0.9996 between targets and outputs. The prediction recoveries were within 99.9~100.0%.

Item Type:Article (Published)
Uncontrolled Keywords:Carbon dioxide; Infrared gas sensors; Neural networks; Bayesian regularization; Levenberg–Marquardt algorithm;
Subjects:Physical Sciences > Detectors
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Research Initiatives and Centres > National Centre for Sensor Research (NCSR)
Official URL:
Copyright Information:Copyright © 2008 Elsevier B.V. All rights reserved.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Environmental Protection Agency, EPA 2005-AIC-MS-43-M4, Science Foundation Ireland, SFI 03/IN.3/1361
ID Code:2963
Deposited On:16 Mar 2009 17:35 by Kim lau. Last Modified 29 Apr 2010 14:33

Download statistics

Archive Staff Only: edit this record