Title :
Forecasting GDP growth based on Ant Colony Clustering Algorithm and RBF neural network
Author :
Zhao, Jianna ; Wang, Xinying ; Wu, Zhuozheng
Author_Institution :
North China Electr. Power Univ., Baoding
Abstract :
In order to forecast GDP growth much more accurate, a hybrid intelligent system is applied to improve the precision of forecasting, which combines ant colony clustering algorithm (ACCA) and RBF neural network. At first, we can make use of ACCA to cluster the data. And then, this clustered data is used to develop classification rules and train RBF neural network. The effectiveness of our methodology was verified by experiment data.
Keywords :
economic forecasting; economic indicators; evolutionary computation; learning (artificial intelligence); optimisation; pattern classification; pattern clustering; radial basis function networks; GDP growth forecasting; Gross Domestic Product; RBF neural network training; ant colony clustering algorithm; data classification rule; evolutionary algorithm; intelligent system; Ant colony optimization; Automation; Clustering algorithms; Economic forecasting; Economic indicators; Logistics; Mathematical model; Neural networks; Predictive models; Radial basis function networks; GDP forecast; RBF neural network; ant colony Algorithm;
Conference_Titel :
Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-2502-0
Electronic_ISBN :
978-1-4244-2503-7
DOI :
10.1109/ICAL.2008.4636457
