Simulation of nonlinear strutures with artificial neural networks

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Description

Structural system simulation is important in analysis, design, testing, control, and other areas, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks offer a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and simulation a much simpler problem. A numerical example is also presented.

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4 p.

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Paez, T.L. March 1, 1996.

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

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Description

Structural system simulation is important in analysis, design, testing, control, and other areas, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks offer a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and simulation a much simpler problem. A numerical example is also presented.

Physical Description

4 p.

Notes

OSTI as DE96006764

Source

  • 11. American Society of Civil Engineers (ASCE) engineering mechanics conference, Ft. Lauderdale, FL (United States), 19-22 May 1996

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  • Other: DE96006764
  • Report No.: SAND--96-0405C
  • Report No.: CONF-9605100--1
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 208360
  • Archival Resource Key: ark:/67531/metadc672098

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • April 14, 2016, 12:42 p.m.

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Paez, T.L. Simulation of nonlinear strutures with artificial neural networks, article, March 1, 1996; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc672098/: accessed October 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.