Proc. SPIE. 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
KEYWORDS: Statistical analysis, Digital filtering, Particles, Monte Carlo methods, Particle filters, System identification, Electronic filtering, Nonlinear filtering, Systems modeling, Filtering (signal processing)
The most common choice of importance density is the transition prior density function for particle filter (it is also known as SIR filter, Monte Carlo filter, Bayesian bootstrap filter, condensation, etc.), since it is intuitive and simple to implement, but using the prior as the importance density suffers from drawback of without any knowledge of the observations, and hence the state space is explored without direct knowledge of the observations, maybe lead to poor performance for the particle filtering. To accomplish this, it is necessary to incorporate the current observation in the importance density. In this paper, we propose an auxiliary particle filter (APF) method to identify a non-stationary dynamic system with abrupt change of system parameters. In the APF, the importance density is proposed as a mixture density that depends upon the past state and the most recent observations, and hence which has a good time tracking ability is more suitable for tracking the non-stationary system than the conventional particle filters. The numerical simulations confirm effectiveness of the proposed method for the structural system identification.
In this paper we propose a neural network-based approach for damage detection of unknown structure systems. Newly developed global H<sub>∞</sub> Filter optimal learning algorithm for the neural network to simulate a structural response is developed. This algorithm is based on the worst-case disturbances design criterion, and is therefore robust with respect to model uncertainties and lack of statistical information to the exogenous signals. Simulation results are presented to identify dynamic response characteristics of nonlinear structural systems corresponding to different degrees of parameters changes, which indicate that damage occurred in the structure. It is shown that the proposed method is highly robust and more appropriate in practical early structural damage detection.