Title :
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
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;
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
DOI :
10.1109/WCICA.2008.4593806
