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

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.