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Proceedings Paper

Efficient system identification algorithm using Monte Carlo filter and its application
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Paper Abstract

In this paper, we develop a new structural identification algorithm by improving the defect of the classical Monte Carlo Filter (MCF). In the MCF, we identify the probability density function of the state vector which is approximated by many realizations, called particles. In the classical MCF, however, as the degree of freedom of structural model increases, we have to generate exponential order of particles. This results in extreme increase of computation time. To overcome this problem, we developed the relaxation MCF (RMCF) in which we improve the filtering process of the classical MCF. By using this method, we can reduce computation time drastically. Moreover, we developed the GA-RMCF, in which we combine the Genetic Algorithm (GA) with the RMCF. We apply the proposed algorithm, the GA-RMCF, to identifying the dynamic parameters of a five-story model building using observed data obtained through the shaking table tests. The data processed here are from a linear structural model.

Paper Details

Date Published: 21 July 2004
PDF: 11 pages
Proc. SPIE 5394, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III, (21 July 2004); doi: 10.1117/12.541737
Show Author Affiliations
Yohei Tanaka, Kyoto Univ. (Japan)
Tadanobu Sato, Kyoto Univ. (Japan)


Published in SPIE Proceedings Vol. 5394:
Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III
Tribikram Kundu, Editor(s)

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