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Nonlinear Structural Dynamical System Identification Using Adaptive Particle Filters by Vikas Namdeo; C.S. Manohar is a Engineering article available to read on EtoBox.

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The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibratio

Who reads Nonlinear Structural Dynamical System Identification Using Adaptive Particle Filters?

Author

Vikas Namdeo; C.S. Manohar

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 0022-460X)

Published

2007

Language

EN

Field

Engineering (Physical Sciences)