Experimental Identification of a Flow Orifice Using a Neural Network and the Conjugate Gradient Method

[+] Author and Article Information
X. P. Xu, R. T. Burton, C. M. Sargent

Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, S7NOWO Canada

J. Dyn. Sys., Meas., Control 118(2), 272-277 (Jun 01, 1996) (6 pages) doi:10.1115/1.2802314 History: Received March 28, 1994; Online December 03, 2007


An experimental approach of using a neural network model to identifying a nonlinear non-pressure-compensated flow valve is described in this paper. The conjugate gradient method with Polak-Ribiere formula is applied to train the neural network to approximate the nonlinear relationships represented by noisy data. The ability of the trained neural network to reproduce and to generalize is demonstrated by its excellent approximation of the experimental data. The training algorithm derived from the conjugate gradient method is shown to lead to a stable solution.

Copyright © 1996 by The American Society of Mechanical Engineers
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