Particle swarm optimization for structural system identification

System identification is an inverse problem of using measured data from a system to estimate quantities that give a complete description of the system according to some representative model of it. Difficulties lie in the development of algorithms that use measured data from the system to characterize it. without significant a priori knowledge of the system. A method for identification of structural systems using Particle swarm optimization (PSO) algorithm is presented to overcome some of the difficulties encountered in the field. The PSO algorithm is a new evolutionary computation method which is applicable to complex optimization problems that are nonlinear, nondifferentiable and multimodal. The basic idea of the method is that the identification problems are cast as a multimodal nonlinear programming problem, and then particle swarm optimization algorithm is used to find the optimal estimation of the parameters. Some results obtained with this algorithm are presented for the identification of structural systems under conditions including limited input/output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness of the system. The numerical examples show that the PSO method is easy to implement, computationally inexpensive, and is successful for structural system identification.