Characterization of nonlinear dynamic systems using artificial neural networks

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The efficient characterization of nonlinear systems is an important goal of vibration and model testing. The authors build a nonlinear system model based on the acceleration time series response of a single input, multiple output system. A series of local linear models are used as a template to train artificial neutral networks (ANNs). The trained ANNs map measured time series responses into states of a nonlinear system. Another NN propagates response states in time, and a third ANN inverts the original map, transforming states into acceleration predictions in the measurement domain. The technique is illustrated using a nonlinear oscillator, in ... continued below

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10 p.

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Urbina, A.; Hunter, N.F. & Paez, T.L. December 1, 1998.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Urbina, A. Univ. of Texas, El Paso, TX (United States)
  • Hunter, N.F. Los Alamos National Lab., NM (United States). Engineering Science and Analysis Div.
  • Paez, T.L. Sandia National Labs., Albuquerque, NM (United States). Experimental Structural Dynamics Dept.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

The efficient characterization of nonlinear systems is an important goal of vibration and model testing. The authors build a nonlinear system model based on the acceleration time series response of a single input, multiple output system. A series of local linear models are used as a template to train artificial neutral networks (ANNs). The trained ANNs map measured time series responses into states of a nonlinear system. Another NN propagates response states in time, and a third ANN inverts the original map, transforming states into acceleration predictions in the measurement domain. The technique is illustrated using a nonlinear oscillator, in which quadratic and cubic stiffness terms play a major part in the system`s response. Reasonable maps are obtained for the states, and accurate, long-term response predictions are made for data outside the training data set.

Physical Description

10 p.

Notes

OSTI as DE99001064

Source

  • 69. shock and vibration symposium, Minneapolis, MN (United States), 12 Oct 1998

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  • Other: DE99001064
  • Report No.: SAND--98-2135C
  • Report No.: LA-UR--98-2945;CONF-981031--
  • Grant Number: AC04-94AL85000;W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 291159
  • Archival Resource Key: ark:/67531/metadc685209

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Creation Date

  • December 1, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • May 5, 2016, 8:46 p.m.

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Urbina, A.; Hunter, N.F. & Paez, T.L. Characterization of nonlinear dynamic systems using artificial neural networks, article, December 1, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc685209/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.