A forecasting model of RBF neural network based on genetic algorithms optimization

Author

Yumin Pan ; Weining Xue ; Quanzhu Zhang ; Liyong Zhao

Author_Institution

Dept. of Electron. Inf. Eng., North China Inst. of Sci. & Technol., Beijing, China

Volume

1

fYear

2011

fDate

26-28 July 2011

Firstpage

48

Lastpage

51

Abstract

A new method of gas emission forecasting based on the optimized RBF network is presented. In this method, genetic algorithm (GA) is applied to optimize the position of data centers, widths, and weights of the RBF network, so forming a GA-RBF model. The principle and algorithms of neural network are introduced. The simulation results show that the improved RBF neural networks has high precision, with reliable accuracy, good convergence rate and fast network training speed. Compared with the traditional RBF and BP networks, the method is more efficient and feasible.

Keywords

air pollution; convergence; forecasting theory; genetic algorithms; mining; radial basis function networks; GA-RBF model; RBF neural network; gas emission forecasting; genetic algorithm optimization; network training speed; Coal; Forecasting; Genetic algorithms; Optimization; Predictive models; Radial basis function networks; Training; RBF neural networks; forecasting; gas emission; genetic algorithm (GA);

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2011 Seventh International Conference on

Conference_Location

Shanghai

ISSN

2157-9555

Print_ISBN

978-1-4244-9950-2

Type

conf

DOI

10.1109/ICNC.2011.6022042

Filename

6022042