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GA-MCF, the number of particles set used for mutation is 100 Fig 3 shows comparison of the time histories of
the identified circular frequency of the first layer obtained by the normal MCF and that by GA-MCF The
identified result obtained by the GA-MCF has higher tracking ability for the non-stationary change of structural
parameters than that by normal MCF Fig 4 shows the number of particles that are replaced by new particles
generated in step (e) of GA-MCF algorithm This means that mutations in GA work well to track abrupt change
of structural parameters The number of renewal particles is large especially when the structural system becomes
non-stationarv
2 4 s
natural circular frequency
{a) normal MCF
^ GA-MCF
Figure 3 Companson of the time histories of the identified natural circular
frequency at node 1 by normal MCF and that by GA-MCF
Figure 4 The number of the renewal particles m GA-MCF
CONCLUSIONS
We developed two new system identification algorithms, one is a hybrid algorithm combining the Kalman Filter
and the Monte Carlo Filter The other is an algorithm taking into account the Genetic Algorithm to speed up
convergence of the Monte Carlo Filter to identify non-stationary structural systems
REFERENCE
[ 1 ] Kitagawa, Genshiro {1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models Journal of
Computational and Graphical Statistics Vol 5, No I 1 -25
[2] Yoshida, Ikumasa (2001) Damage Detection Using Monte Carlo Filter Based on Non-Gaussian Noises 8 th
International Conference on Structural Safety and Reliability June
[3] Higuchu Tomoyuki. (1997) Monte Carlo filter using the Genetic algorithm operators Journal of Statistical
Computation and Simula!ion, Vol 59, No 1 1-23