Update of explicit limit state functions constructed using Support Vector Machines

2007 ◽  
Author(s):  
Anirban Basudhar ◽  
Samy Missoum
Keyword(s):  
Support Vector Machines ◽  
Limit State ◽  
Support Vector ◽  
Vector Machines ◽  
State Functions

Reliability prediction through guided tail modeling using support vector machines

2013 ◽  
Vol 227 (12) ◽  
pp. 2780-2794 ◽  
Author(s):  
Erdem Acar
Keyword(s):  
Support Vector Machines ◽  
Dimension Reduction ◽  
Limit State ◽  
Reliability Prediction ◽  
Support Vector ◽  
Threshold Estimation ◽  
Tail Region ◽  
Generalized Lambda Distribution ◽  
Tail Part ◽  
Vector Machines

Reliability prediction of highly safe mechanical systems can be performed using classical tail modeling. Classical tail modeling is based on performing a relatively small number of limit-state evaluations through a sampling scheme and then fitting a tail model to the tail part of the data. However, the limit-state calculations that do not belong to the tail part are discarded, so majority of limit-state evaluations are wasted. Guided tail modeling, proposed earlier by the author, can provide a remedy through guidance of the limit-state function calculations toward the tail region. In the original guided tail modeling, the guidance is achieved through a procedure based on threshold estimation using univariate dimension reduction and extended generalized lambda distribution and tail region approximation using univariate dimension reduction. This article proposes a new guided tail modeling technique that utilizes support vector machines. In the proposed method, named guided tail modeling with support vector machines (GTM-SVM), the threshold estimation is still performed using univariate dimension reduction and extended generalized lambda distribution, while the tail region approximation is based on support vector machines. The performance of guided tail modeling with support vector machines is tested with mathematical example problems as well as structural mechanics problems with varying number of variables. GTM-SVM is found to be more accurate than both guided tail modeling and classical tail modeling for low-dimensional problems. For high-dimensional problems, on the other hand, the original guided tail modeling is found to be more accurate than guided tail modeling with support vector machines, which is superior to classical tail modeling.

scholarly journals Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Yu Wang ◽  
Xiongqing Yu ◽  
Xiaoping Du
Keyword(s):  
Support Vector Machines ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Limit State ◽  
Support Vector ◽  
State Function ◽  
Limit State Function ◽  
Wing Design ◽  
Aircraft Wing ◽  
Vector Machines

A new reliability-based design optimization (RBDO) method based on support vector machines (SVM) and the Most Probable Point (MPP) is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS) is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA) is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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