By Topic

Parameter estimation using a CLPSO strategy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tang, H. ; Res. Inst. of Struct. Eng. & Disaster Reduction, Tongji Univ., Shanghai ; Zhang, W. ; Fan, C. ; Xue, S.

As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications for solving complex optimization problems in different fields mainly for various continuous optimization problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper utilizes an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.

Published in:

Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on

Date of Conference:

1-6 June 2008