IDENTIFICATION AND UNCERTAINTY ESTIMATION OF STRUCTURAL PARAMETERS

In the application of system identification to a structural system, un known parameters are determined based on the numerical analysis of inp ut and output measurements. The accuracy of an identified parameter an d its uncertainty both depend on the numerical method, measurement noi se and modeling error. Most studies, however, identify parameter means without addressing the issue of parameter uncertainties. Presented in this paper is an improved version of the commonly used extended Kalma n filter (EKF) by incorporating an adaptive filter procedure. The syst em noise covariance is updated in time segments in order to ensure sta tistical consistency between the predicted error covariance and the me an square of actual residuals. Comprising two stages in a cycle, the a daptive EKF method not only identifies the parameter values but also g ives a useful estimate of uncertainties. Two numerical examples of sim ulation with noise are presented. The first example illustrates the su perior statistical performance of the proposed method over the convent ional EKF method. The second example demonstrates the numerical accura cy and efficiency of this method, with and without modeling error, in comparison with a published least-squares approach.