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Structural Engineering and Mechanics
  Volume 76, Number 1, October10 2020 , pages 101-114

Nonlinear identification of Bouc-Wen hysteretic parameters using improved experience-based learning algorithm
Weili Luo, Tongyi Zheng, Huawei Tong, Yun Zhou and Zhongrong Lu

    In this paper, an improved experience-based learning algorithm (EBL), termed as IEBL, is proposed to solve the nonlinear hysteretic parameter identification problem with Bouc-Wen model. A quasi-opposition-based learning mechanism and new updating equations are introduced to improve both the exploration and exploitation abilities of the algorithm. Numerical studies on a single-degree-of-freedom system without/with viscous damping are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of seven lead-filled steel tube dampers is presented and their hysteretic parameters are also successfully identified with normalized mean square error values less than 2.97%. Both numerical and laboratory results confirm that, in comparison with EBL, CMFOA, SSA, and Jaya, the IEBL is superior in nonlinear hysteretic parameter identification in terms of convergence and accuracy even under measurement noise.
Key Words
    experience-based learning; Bouc–Wen model; hysteretic parameters; nonlinear system identification; lead-filled steel tube dampers
Weili Luo, Tongyi Zheng, Huawei Tong, Yun Zhou: School of Civil Engineering, Guangzhou University, Guangzhou, P.R. China
Zhongrong Lu: Department of Applied Mechanics, Sun Yat-sen University, Guangzhou, P.R. China

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