0
TECHNICAL PAPERS

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

Abstract

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
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In