Paper Title:
Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm
  Abstract

Structural damage detection and health monitoring is very important in many applications, and a key related issue is the method of damage detection. In this paper, Fuzzy Least Square Support Vector Machine (FLS-SVM) is constructed by combining Fuzzy Logic with LS-SVM, and a real-coded Quantum Genetic Algorithm (QGA) is applied to optimize parameters of FLS-SVM. Then, the method of FLS-SVM integrated QGA is used to detect damages for fiber smart structures. The testing results show FLS-SVM possesses the higher detecting accuracy and the bitter dissemination ability than LS-SVM under the same conditions, and the parameters of FLS-SVM can be effectively optimized by the real-coded QGA. The proposed method of FLS-SVM integrated QGA is effective and efficient for structural damage detection.

  Info
Periodical
Edited by
Qi Luo
Pages
1365-1371
DOI
10.4028/www.scientific.net/AMM.20-23.1365
Citation
J. H. Xie, "Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm ", Applied Mechanics and Materials, Vols. 20-23, pp. 1365-1371, 2010
Online since
January 2010
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$35.00
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