A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reliability analysis using radial basis function networks and support vector machines
Abstract To reduce computational costs in structural reliability analysis, utilising approximate response surface functions for reliability assessment has been suggested. Based on the similarities of two adaptive and flexible models, the radial basis function neural network (RBFN) and support vector machine (SVM), the derivatives of the approximate functions of RBFN and SVM models with respect to basic variables are given, and two RBFN-based RSMs (RBFN-RSM1, RBFN-RSM2) and two SVM-based RSMs (SVM-RSM1, SVM-RSM2) are studied. The similarities and differences of these methods are reviewed, and the applicability of these methods is illustrated using five examples. It is shown that there is no obvious difference between RBFN-based RSMs and SVM-based RSMs, and the number of samples needed in RBFN/SVM-RSM2 is smaller than that of RBFN/SVM-RSM1.
Reliability analysis using radial basis function networks and support vector machines
Abstract To reduce computational costs in structural reliability analysis, utilising approximate response surface functions for reliability assessment has been suggested. Based on the similarities of two adaptive and flexible models, the radial basis function neural network (RBFN) and support vector machine (SVM), the derivatives of the approximate functions of RBFN and SVM models with respect to basic variables are given, and two RBFN-based RSMs (RBFN-RSM1, RBFN-RSM2) and two SVM-based RSMs (SVM-RSM1, SVM-RSM2) are studied. The similarities and differences of these methods are reviewed, and the applicability of these methods is illustrated using five examples. It is shown that there is no obvious difference between RBFN-based RSMs and SVM-based RSMs, and the number of samples needed in RBFN/SVM-RSM2 is smaller than that of RBFN/SVM-RSM1.
Reliability analysis using radial basis function networks and support vector machines
Tan, Xiao-hui (author) / Bi, Wei-hua (author) / Hou, Xiao-liang (author) / Wang, Wei (author)
Computers and Geotechnics ; 38 ; 178-186
2010-11-07
9 pages
Article (Journal)
Electronic Resource
English
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