Identification of IIR systems using comprehensive learning particle swarm optimisation

Majhi, Babita; Panda, Ganapati

doi:10.1504/ijpec.2009.023478

Cited by 7 publications

(1 citation statement)

References 51 publications

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“…Huang et al (2006) presented a CLPSO based method to handle multiple objective optimization problems. In a recent article (Majhi & Panda, 2009) the CLSPO based algorithm has been applied to identify the feed-forward and feedback coefficients of IIR systems. It is reported that this method outperforms the existing standard recursive LMS (RLMS), GA and PSO based methods in terms of minimum MSE after convergence, execution time and product of population size and number of input samples used in training.…”

Section: Introductionmentioning

confidence: 99%

Usage of Comprehensive Learning Particle Swarm Optimization for Parameter Identification of Structural System

Tang

Xie

Xue

2015

International Journal of Natural Computing Research

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This paper introduces a novel swarm intelligence based algorithm named comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. With the new strategy in this variant of particle swarm optimization (PSO), historical best information for all other particles is used to update a particle's velocity. This means that the particles have more exemplars to learn from, as well as have a larger potential space to fly, avoiding premature convergence. Simulation results for identifying the parameters of a five degree-of-freedom (DOF) structural system under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate improved estimation of these parameters by the CLPSO when compared with those obtained from standard PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a twelve-story shear building shaking table model.

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Section: Introductionmentioning

confidence: 99%