DocumentCode
2662340
Title
Study of prediction based on RBF Neural network optimized by PSO
Author
Lianghai, Wu ; Yiming, Chen
Author_Institution
Dept. of Exp. Teaching, Maoming Univ., Maoming, China
Volume
6
fYear
2010
fDate
16-18 April 2010
Abstract
The parameters of RBF neural network have important effect to its performance. The parameters selection is the important research content of the RBF Neural network. For this problem, this study proposed a kind of method to choose the parameters of the RBF neural network by particle swarm optimization algorithm (PSO). The experiment result indicates the RBF neural network prediction model optimized by PSO has high prediction accuracy, and PSO is one kind of effective method for RBF neural network parameters selection.
Keywords
demand forecasting; particle swarm optimisation; petroleum; radial basis function networks; RBF neural network optimisation; particle swarm optimization algorithm; petroleum demand; prediction accuracy; Accuracy; Demand forecasting; Economic forecasting; Neural networks; Neurons; Optimization methods; Particle swarm optimization; Petroleum; Predictive models; Production; RBF neural network; particle swarm optimization; petroleum demand; prediction model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486107
Filename
5486107
Link To Document