Jc. Patra et A. Van Den Bos, Modeling of an intelligent pressure sensor using functional link artificial neural networks, ISA TRANS, 39(1), 2000, pp. 15-27
A capacitor pressure sensor (CPS) is modeled for accurate readout of applie
d pressure using a novel artificial neural network (ANN). The proposed func
tional link ANN (FLANN) is a computationally efficient nonlinear network an
d is capable of complex nonlinear mapping between its input and output patt
ern space. The nonlinearity is introduced into the FLANN by passing the inp
ut pattern through a functional expansion unit. Three different polynomials
such as, Chebyschev, Legendre and power series have been employed in the F
LANN. 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 degrees C, the maximum error of estimation of pressure remains wi
thin +/-3%. With the help of computer simulation, the performance of the th
ree types of FLANN models has been compared to that of an MLP based model.
(C) 2000 Elsevier Science Ltd. All rights reserved.