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identification of time-varying structural dynamic systems - an artificial intelligence approachAn application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.
Document ID
19920054230
Document Type
Reprint (Version printed in journal)
Authors
Glass, B. J.
(NASA Ames Research Center Moffett Field, CA; Georgia Institute of Technology, Atlanta, United States)
Hanagud, S.
(Georgia Institute of Technology Atlanta, United States)
Date Acquired
August 15, 2013
Publication Date
May 1, 1992
Publication Information
Publication: AIAA Journal
Volume: 30
Issue: 5 Ma
ISSN: 0001-1452
Subject Category
CYBERNETICS
Funding Number(s)
CONTRACT_GRANT: DAAG29-82-K-0094
Distribution Limits
Public
Copyright
Other