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Functional link neural network-based intelligent sensors for harsh environments

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Functional link neural network-based intelligent sensors for harsh environments

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dc.contributor.author Patra, Jagdish Chandra
dc.contributor.author Chakraborty, Goutam
dc.contributor.author Mukhopadhyay, Subhas
dc.date.accessioned 2011-10-13T01:05:28Z
dc.date.available 2011-10-13T01:05:28Z
dc.date.copyright 2008
dc.date.issued 2011-10-13
dc.identifier.citation Patra, J. C., Chakraborty, G., & Mukhopadhyay, S. (2008). Functional Link Neural Network-based Intelligent Sensors for Harsh Environments. Sensors & Transducers Journal, 90, 209-220.
dc.identifier.issn 1726-5479
dc.identifier.uri http://hdl.handle.net/10220/7254
dc.description.abstract As the use of sensors is wide spread, the need to develop intelligent sensors that can automatically carry out calibration, compensate for the nonlinearity and mitigate the undesirable influence of the environmental parameters, is obvious. Smart sensing is needed for accurate and reliable readout of the measurand, especially when the sensor is operating in harsh environments. Here, we propose a novel computationally-efficient functional link neural network (FLNN) that effectively linearizes the response characteristics, compensates for the nonidealities, and calibrates automatically. With an example of a capacitive pressure sensor and through extensive simulation studies, we have shown that the performance of the FLNN-based sensor model is similar to that of a multilayer perceptron (MLP)-based model although the former has much lower computational requirement. The FLNN model is capable of producing linearized readout of the applied pressure with a full-scale error of only ±1.0% over a wide operating range of −50 to 200 ˚C.
dc.format.extent 20 p.
dc.language.iso en
dc.relation.ispartofseries Sensors & transducers journal
dc.rights © 2008 IFSA
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
dc.title Functional link neural network-based intelligent sensors for harsh environments
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.openurl http://www.sensorsportal.com/HTML/DIGEST/P_SI_38.htm
dc.description.version Accepted version
dc.identifier.rims 131267

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