计算力学学报2026，Vol.43Issue(1)：145-150,6.DOI:10.7511/jslx20241027001
基于改进中心差分卡尔曼算法的滞回模型参数识别
Identification of hysteresis model parameters based on improved central difference Kalman filter algorithm
摘要
Abstract
Under the action of dynamic loads,civil engineering structures exhibit significant nonlinear hysteresis characteristics.The Cenral Difference Kalman Filter(CDKF)algorithm can effectively identify system states and model parameters.Traditional CDKF algorithms use Cholesky decomposition(Cholupdate)to solve the square root of the covariance matrix,which must ensure that the covariance matrix is positive definite and may cause interruption in the recursive process.This paper proposes an improved center difference Kalman algorithm,which uses QR decomposition instead of Cholupdate to overcome the strict requirement that the covariance matrix must be positive definite,making the recursive calculation process unconditionally mathematically stable.Numerical calculations were performed on the Bouc Wen model system under different noise conditions,and the results showed that the improved central difference Kalman filter algorithm can accurately identify the state and model parameters of the hysteresis system,and has strong stability and noise resistance.关键词
Bouc-Wen滞回模型/中心差分卡尔曼算法/QR分解/参数识别Key words
Bouc-Wen hysteresis model/central differential Kalman algorithm/QR decomposition/parameter identification分类
建筑与水利引用本文复制引用
王向平..基于改进中心差分卡尔曼算法的滞回模型参数识别[J].计算力学学报,2026,43(1):145-150,6.基金项目
重庆市建设科技计划(城科字2024第5-4号)资助项目. （城科字2024第5-4号）
