Journal
SENSORS AND ACTUATORS B-CHEMICAL
Volume 114, Issue 1, Pages 334-343Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2005.05.022
Keywords
humidity sensing; porous silicon humidity sensor; hysteresis effect; compensation of hysteresis effect using ANN; hardware implementation of ANN model
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This paper presents a simple technique based on well-known multilayer perceptron (MLP) neural network with back propagation training algorithm for compensating the significant error due to hysteresis in a porous silicon relative humidity sensor. The porous silicon humidity sensor has been fabricated, and its hysteresis with increasing and decreasing relative humidity has been determined experimentally by a novel phase detection circuit. Simulation studies show that the artificial neural network (ANN) technique can be effectively used to compensate the hysteresis of the porous silicon sensor for relative humidity (%RH) measurement. A hardware implementation scheme of the hysteresis compensating ANN model using a micro-controller is also proposed. Simulation studies show that the maximum error is within +/- 1% of its full-scale value. (c) 2005 Elsevier B.V. All rights reserved.
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