›› 2008, Vol. 29 ›› Issue (5): 1205-1209.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

A RBF neural network coupling algorithm based on MPSO for parameter identification of piles in dynamic testing

GUO Jian1, 2, WANG Yuan-han1, MIAO Yu1   

  1. 1. College of Civil Engineering ＆ Mechanics, Huazhong University of Science ＆ Technology, Wuhan 430074, China; 2. Zhongnan Branch, Wuhan University of Science and Technology, Wuhan 430074, China
  • Received:2007-08-02 Online:2008-05-10 Published:2013-07-24

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Abstract: Mutation particle swarm optimization (MPSO) is a kind of improved stochastic global optimization based on swarm intelligence. The advantages of MPSO are that the probability falling into the local extreme values can be reduced; and the global optimal searching capability is improved. A new algorithm which combined MPSO with radial basis function (RBF) is presented. It not only has the advantage of the global optimization of MPSO, but also has local accurate searching of RBF. Numerical example shows that the presented method can solve the problem which includes multi-parameters identification and nonlinear optimization problem. This approach has the characteristics of global convergence. The intelligent algorithm is simple and precise.

Key words: MPSO, neural network, dynamic testing, parameter identification

CLC Number: 

  • TU 473
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
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