Geology Reference
In-Depth Information
Chapter 13
Fuzzy Identification
of Seismically Excited
Smart Systems
JinSeop Kim
Massachusetts Institute of Technology, USA
Yeesock Kim
Worcester Polytechnic Institute, USA
Tahar El-Korchi
Worcester Polytechnic Institute, USA
ABSTRACT
In this chapter, a nonlinear modeling framework to identify nonlinear behavior of smart structural
systems under seismic excitations is proposed. To this end, multi-input-multi-output (MIMO) autoregres-
sive exogenous (ARX) input models and Takagi-Sugeno (TS) fuzzy models are coalesced as the MIMO
ARX-TS fuzzy model. The premised part of the proposed MIMO ARX-TS fuzzy model is optimized using
the hierarchical clustering (HRC) algorithm, while its consequent parameters are optimized via the
weighted linear least squares estimation. The performance of the proposed model is investigated using
the dynamic response of a three-story shear planer frame structure equipped with a magnetorheological
(MR) damper subject to earthquake disturbances. Furthermore, the impact of the HRC algorithm on the
performance of the MIMO ARX-TS fuzzy model is compared with that of the subtractive and the fuzzy
C-means clustering algorithms. The equivalence of the original and identified data is numerically shown
to prove that the HRC MIMO ARX-TS fuzzy model introduced here is effective in estimating nonlinear
behavior of a seismically excited building-MR damper system.
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