Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm

Abstract:

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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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Price:

$35.00

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