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Modeling of an intelligent pressure sensor using functional link artificial neural networks.

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Modeling of an intelligent pressure sensor using functional link artificial neural networks.

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Title: Modeling of an intelligent pressure sensor using functional link artificial neural networks.
Author: Patra, Jagdish Chandra.; Van den Bos, Adriaan.
Copyright year: 2000
Abstract: A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from −50 to 150°C, the maximum error of estimation of pressure remains within ±3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.
Subject: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation.
Type: Journal Article
Series/ Journal Title: ISA transactions
School: School of Computer Engineering
Rights: © 2000 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by ISA Transactions, Elsevier.  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/10.1016/S0019-0578(99)00035-X].
Version: Accepted version

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