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Structure-unknown non-linear dynamic systems: identification through neural networks

Masri, S. F. and Chassiakos, A. G. and Caughey, T. K. (1992) Structure-unknown non-linear dynamic systems: identification through neural networks. Smart Materials and Structures, 1 . pp. 45-56. ISSN 0964-1726. http://resolver.caltech.edu/CaltechAUTHORS:MASsms92

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Abstract

Explores the potential of using parallel distributed processing (neural network) approaches to identify the internal forces of structure-unknown non-linear dynamic systems typically encountered in the field of applied mechanics. The relevant characteristics of neural networks, such as the processing elements, network topology, and learning algorithms, are discussed in the context of system identification. The analogy of the neural network procedure to a qualitatively similar non-parametric identification approach, which was previously developed by the authors for handling arbitrary non-linear systems, is discussed. The utility of the neural network approach is demonstrated by application to several illustrative problems.


Item Type:Article
Additional Information:© 1992 IOP Publishing Ltd. Received 24 November 1991, accepted for publication 6 December 1991.
Record Number:CaltechAUTHORS:MASsms92
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:MASsms92
Alternative URL:http://stacks.iop.org/0964-1726/1/45
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:361
Collection:CaltechAUTHORS
Deposited By: Thomas Kirk Caughey
Deposited On:06 Jun 2005
Last Modified:26 Dec 2012 08:39

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