Particle relaxation method of Monte Carlo filter for structure system identification

Abstract

In this paper, we apply Monte Carlo filter (MCF) to identify dynamic parameters of structural systems and improve the efficiency of its algorithm. The algorithms using MCF so far have not been practical for applying to structural identification of large-scale systems because computation time increases exponentially as the degrees of freedom of the system increases. To overcome this problem, we developed a method with the ability to reduce the number of particles which express possible structural response state vector. In MCF, there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo filter (RMCF) and demonstrated its efficiency to identify large degree of freedom systems. Moreover, to increase searching field and speed up convergence time of structural parameters, we proposed an algorithm combining the genetic algorithm with RMCF and named GARMCF.