Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/94242
Title: Intelligent sensors using computationally efficient Chebyshev neural networks
Authors: Juhola, M.
Patra, Jagdish Chandra
Meher, Pramod Kumar
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Issue Date: 2008
Source: Patra, J. C., Juhola, M., & Meher, P. K. (2008). Intelligent sensors using computationally efficient Chebyshev neural networks. IET Science, Measurement & Technology, 2(2), 68-75.
Series/Report no.: IET science, measurement & technology
Abstract: Intelligent signal processing techniques are required for auto-calibration of sensors, and to take care of nonlinearity compensation and mitigation of the undesirable effects of environmental parameters on sensor output. This is required for accurate and reliable readout of the measurand, especially when the sensor is operating in harsh operating conditions. A novel computationally efficient Chebyshev neural network (CNN) model that effectively compensates for such non-idealities, linearises and calibrates automatically is proposed. By taking an example of a capacitive pressure sensor, through extensive simulation studies it is shown that performance of the CNN-based sensor model is similar to that of a multilayer perceptron-based model, but the former has much lower computational requirement. The CNN model is capable of producing pressure readout with a full-scale error of only plusmn1.0% over a wide operating range of -50 to 200degC.
URI: https://hdl.handle.net/10356/94242
http://hdl.handle.net/10220/7105
ISSN: 1751-8822
DOI: 10.1049/iet-smt:20070061
Schools: School of Computer Engineering 
Rights: © 2008 IET. This is the author created version of a work that has been peer reviewed and accepted for publication by IET Science, Measurement & Technology, The Institution of Engineering and Technology.  It incorporates referee's comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.  The published version is available at: [DOI: http://dx.doi.org/10.1049/iet-smt:20070061].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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