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
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;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5486107
