Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems

Wang, Hu; Li, Enying; Li, G.
March 2011
Computational Mechanics;Mar2011, Vol. 47 Issue 3, p251
Academic Journal
This paper presents a crashworthiness design optimization method based on a metamodeling technique. The crashworthiness optimization is a highly nonlinear and large scale problem, which is composed various nonlinearities, such as geometry, material and contact and needs a large number expensive evaluations. In order to obtain a robust approximation efficiently, a probability-based least square support vector regression is suggested to construct metamodels by considering structure risk minimization. Further, to save the computational cost, an intelligent sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). In this paper, a cylinder, a full vehicle frontal collision is involved. The results demonstrate that the proposed metamodel-based optimization is efficient and effective in solving crashworthiness, design optimization problems.


Related Articles

  • A Nonlinear Optimization Technique of Tunnel Construction Based on DE and LSSVM. Xing Jun; Jiang Annan; Wen Zhiwu; Qiu Jingping // Mathematical Problems in Engineering;2013, p1 

    Tunnel construction is a dynamic controlling system with observability and controllability; the feedback analysis requires identifying geophysics parameters and adjusting supporting parameters, and both of them are optimisation problems. The paper proposed a nonlinear optimization technique...

  • Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis. Haihua Yao; Jizheng Chu // Chemical Engineering Research & Design: Transactions of the Inst;Dec2012, Vol. 90 Issue 12, p2247 

    Like any other production processes, atmospheric distillation of crude oil is too complex to be accurately described with first principle models, and on-site experiments guided by some statistical optimization method are often necessary to achieve the optimum operating conditions. In this study,...

  • A Nonlinear Adaptive Beamforming Algorithm Based on Least Squares Support Vector Regression. Lutao Wang; Gang Jin; Zhengzhou Li; Hongbin Xu // Sensors (14248220);2012, Vol. 12 Issue 9, p12424 

    To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this...

  • Support Vector Machine Models for Prediction of Flow Number of Asphalt Mixtures. Ke-zhen Yan; Dong-dong Ge; Zou Zhang // International Journal of Pavement Research & Technology;Jan2014, Vol. 7 Issue 1, p31 

    Permanent deformation is one of the most critical distress types affecting the serviceability of flexible pavement structures. Predicting the rutting potential of asphalt concrete is a complicated task. Flow number (Fn of asphalt mixture is an explanatory index for the evaluation of rutting....

  • Consistency and Localizability. Zakai, Alon; Ritov, Ya'acov // Journal of Machine Learning Research;4/1/2009, Vol. 10 Issue 4, p827 

    We show that all consistent learning methods--that is, that asymptotically achieve the lowest possible expected loss for any distribution on (X,Y)--are necessarily localizable, by which we mean that they do not significantly change their response at a particular point when we show them only the...

  • Optimization of an SVM QP Problem Using Mixed Variable Nonlinear Polynomial Kernel Map and Mixed Variable Unimodal Functions. Tokg√∂z, Emre; Trafalis, Theodore B. // WSEAS Transactions on Systems & Control;Jan2012, Vol. 7 Issue 1, p16 

    Support Vector Machines (SVM) can be constructed with the selection of an appropriate kernel function to solve an optimization problem. Algorithmic approaches can be taken to solve problems related to SVM which are used for regression analysis and data classification of a data set. The...

  • Single Directional SMO Algorithm for Least Squares Support Vector Machines. Xigao Shao; Kun Wu; Bifeng Liao // Computational Intelligence & Neuroscience;2013, Special section p1 

    Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we...

  • LSSVM Parameters Tuning with Enhanced Artificial Bee Colony. Mustaffa, Zuriani; Yusof, Yuhanis // International Arab Journal of Information Technology (IAJIT);May2014, Vol. 11 Issue 3, p236 

    To date, exploring an efficient method for optimizing Least Squares Support Vector Machines (LSSVM) hyper-parameters has been an enthusiastic research area among academic researchers. LSSVM is a practical machine learning approach that has been broadly utilized in numerous fields. To guarantee...

  • Study on Classification Performance and Parameter Optimization of Least Squares Support Vector Machine. Fugang YANG; Yuling WANG; Yongqiang WANG // International Journal of Advancements in Computing Technology;Jul2012, Vol. 4 Issue 12, p152 

    It is a time-consuming and memory consumption process to get the optimal model parameters through training samples when Least Squares Support Vector Machine (LS-SVM) classifies large datasets. Therefore, we carry out some experiments to find the reasons that affect the classification performance...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics