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An Intelligent Nonlinear System Identification Method With Robust State Estimation

[+] Author Affiliations
Jason R. Kolodziej

Rochester Institute of Technology, Rochester, NY

Paper No. DSCC2011-5970, pp. 361-367; 7 pages
doi:10.1115/DSCC2011-5970
From:
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5475-4
  • Copyright © 2011 by ASME

abstract

An approach is presented for nonlinear system identification by combining a proven state estimation technique, Minimum Model Error (MME) estimation, with a feed-forward neural network. The MME/NN hybrid algorithm first determines from measurement data a smooth state estimate of the system as well as identifying a correction to the assumed system model. Next, the state estimates are then used as training data for the neural network’s representation of the unmodeled system dynamics. The performance is then shown to be an improvement over a stand alone black-box neural network.

Copyright © 2011 by ASME

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