A hybrid model used to predict flow stress

Author

Bing, Wu ; Yan-Ping, Wang

Author_Institution

Sch. of Sci., Shandong Univ. of Technol., Zibo, China

Volume

2

fYear

2010

fDate

21-24 May 2010

Abstract

To improved the prediction accuracy of the flow stress, a hybrid model based on the Hybrid Least Squares Support Vector Machine (HLS-SVM) and Mathematical Models (MM) was proposed. In HLS-SVM model, the optimal parameters of LS-SVM were obtained by self-adaptive Particle Swarm Optimization (PSO)based on Simulated Annealing (SA). Simulation experiment results revealed that this model could correctly recur to the flow stress in the sample data and accurately predict the non-sample data. The efficiency and accuracy of the predicted flow stress achieved by the proposed model were better than the methods used in most literature.

Keywords

least squares approximations; mechanical engineering computing; particle swarm optimisation; plastic flow; simulated annealing; support vector machines; LS-SVM optimal parameters; MM; PSO; data sampling; flow stress prediction; hybrid least squares support vector machine; hybrid model; mathematical models; self-adaptive particle swarm optimization; simulated annealing; Accuracy; Deformable models; Least squares methods; Mathematical model; Particle swarm optimization; Predictive models; Simulated annealing; Support vector machines; Thermal resistance; Thermal stresses; flow stress; least square support vector machine; particle swarm optimization; simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Future Computer and Communication (ICFCC), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5821-9

Type

conf

DOI

10.1109/ICFCC.2010.5497620

Filename

5497620