Application of optimizing the parameters of SVM using genetic simulated annealing algorithm

Author

Longhan Cao ; Shanquan Zhou ; Rui Li ; Fan Wu ; Tao Liu

Author_Institution

Key Lab. of Control Eng., Chongqing Commun. Inst., Chongqing

fYear

2008

fDate

25-27 June 2008

Firstpage

5381

Lastpage

5385

Abstract

The genetic simulated annealing algorithm can get global solution with low computational load. By means of this algorithms optimization method, the support vector machines (SVM) radial basis probabilistic kernel parameters of the performance was found out. A special software was developed on this method, it can be used in different field and improved the application of SVM in industry area. Then, a model of the battery capacity was established, and its correctness was tested by contrast with cross validation.

Keywords

genetic algorithms; probability; simulated annealing; support vector machines; SVM; battery capacity; genetic simulated annealing algorithm; parameter optimisation; radial basis probabilistic kernel; support vector machine; Application software; Batteries; Computational modeling; Computer industry; Genetics; Kernel; Optimization methods; Simulated annealing; Support vector machines; Testing; SVM; battery capacity; genetic simulated annealing algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593806

Filename

4593806