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Comprehensive Learning Particle Swarm Optimization for Structural System Identification

Comprehensive Learning Particle Swarm Optimization for Structural System Identification

Hesheng Tang, Xueyuan Guo, Lijun Xie, Songtao Xue
Copyright: © 2018 |Pages: 25
ISBN13: 9781522550204|ISBN10: 1522550208|EISBN13: 9781522550211
DOI: 10.4018/978-1-5225-5020-4.ch002
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MLA

Tang, Hesheng, et al. "Comprehensive Learning Particle Swarm Optimization for Structural System Identification." Incorporating Nature-Inspired Paradigms in Computational Applications, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 51-75. https://doi.org/10.4018/978-1-5225-5020-4.ch002

APA

Tang, H., Guo, X., Xie, L., & Xue, S. (2018). Comprehensive Learning Particle Swarm Optimization for Structural System Identification. In M. Khosrow-Pour, D.B.A. (Ed.), Incorporating Nature-Inspired Paradigms in Computational Applications (pp. 51-75). IGI Global. https://doi.org/10.4018/978-1-5225-5020-4.ch002

Chicago

Tang, Hesheng, et al. "Comprehensive Learning Particle Swarm Optimization for Structural System Identification." In Incorporating Nature-Inspired Paradigms in Computational Applications, edited by Mehdi Khosrow-Pour, D.B.A., 51-75. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5020-4.ch002

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Abstract

This chapter introduces a novel swarm-intelligence-based algorithm named the comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which is formulated as a high-dimensional multi-modal numerical optimization problem. 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 and 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 CLPSO when compared with those obtained from PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a 12-story shear building shaking table model.

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