By Topic

GDP prediction by support vector machine trained with genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Gang Long ; Econ. & Manage. Sch., Wuhan Univ., Wuhan, China

In the study, support vector machine trained with genetic algorithm is applied in GDP forecasting. Genetic algorithm can get optimal solution in short time, which is an excellent method in parameters selection of support vector machine. Then, genetic algorithm is introduced to simultaneously optimize the SVM parameters. The total GDP data of Anhui province from 1989 to 2007 are employed to compare the forecasting performance of the proposed GA-SVM model and RBF neural network GDP forecasting model. It is indicated that GDP prediction performance of the proposed GA-SVM is better than that of RBFNN.

Published in:

Signal Processing Systems (ICSPS), 2010 2nd International Conference on  (Volume:3 )

Date of Conference:

5-7 July 2010