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A three‐stage identification approach for hysteretic systems
Author(s) -
Loh ChinHsiung,
Chung ShengTsai
Publication year - 1993
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.4290220204
Subject(s) - gauss , identification (biology) , noise (video) , control theory (sociology) , system identification , least squares function approximation , kalman filter , convergence (economics) , basis (linear algebra) , stage (stratigraphy) , computer science , algorithm , mathematics , data modeling , statistics , physics , artificial intelligence , control (management) , paleontology , botany , geometry , quantum mechanics , database , estimator , economics , image (mathematics) , biology , economic growth
This paper deals with the identification of the parameters of a smoothed hysteretic model which was proposed by Bouc and Wen with emphasis on restoring force hysteresis. The problem of estimating the parameters of this system on the basis of input‐output data, possibly noise corrupted, is considered. Through the application of various simulated time histories from the hysteretic model, a three‐stage systematic method of system identification was proposed. Four different methods of identification are arranged and conducted in this three‐stage system identification. The first stage, a sequential regressional analysis is used to identify the equivalent linear system from which elastic or inelastic response can be identified. The identified parameters can be used in the stage when the system is in elastic response. In the second stage, both time domain least‐squares method and Gauss‐Newton method are applied. The convergence of the Gauss‐Newton method can be guaranteed if the identified results from least‐squares method are adopted as the initial values for Gauss‐Newton method. In the third stage, the extended Kalman filtering technique is needed to identify the noise‐corrupt data. Application of this algorithm to a SDOF non‐deteriorating system is verified.