Title :
Water demand prediction based on RBF neural network
Author :
Wang, Yimin ; Zhang, Jue
Author_Institution :
Xi´´an Univ. of Technol., Xi´´an
Abstract :
Neural-network-based method of forecasting is presented in this paper. Simplified rival penalized competitive learning method (SRPCL) to make an adaptive clustering of networkspsila input pattern is developed. The objective function is established to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to water demand prediction in the Yellow river basin. The results of numerical simulations demonstrate the effectiveness of the method.
Keywords :
forecasting theory; learning (artificial intelligence); pattern clustering; prediction theory; radial basis function networks; rivers; water resources; RBF neural network; Yellow river basin; adaptive clustering; forecasting method; simplified rival penalized competitive learning method; water demand prediction; Automation; Decision support systems; Intelligent control; Neural networks; Hidden nodes; RBF neural-network; SRPCL;
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.2008.4594527
