Gas content prediction based on GA-RBF neural network

Author

Zhai, Bo ; Shan, Jianfeng

Author_Institution

Sch. of Comput. & Commun. Eng., Liaoning Shihua Univ., Fushun, China

fYear

2010

fDate

26-28 May 2010

Firstpage

3104

Lastpage

3108

Abstract

Genetic algorithms (GA) and radial basis function (RBF) neural network are combined in this paper. Prediction model of gas content in coal seam is set up based on GA-RBF neural network optimized by genetic algorithm in network structure and parameters. The actual forecasting results show that the algorithm has higher prediction accuracy and faster computing speed and is helpful to mine gas disaster prediction and prevention.

Keywords

air pollution; disasters; forecasting theory; gas industry; genetic algorithms; radial basis function networks; coal seam; gas content prediction model; genetic algorithms; mine gas disaster prediction; mine gas disaster prevention; network parameters; network structure; radial basis function neural network; Accuracy; Feeds; Function approximation; Gaussian processes; Genetic algorithms; Network topology; Neural networks; Neurons; Optimization methods; Radial basis function networks; RBF; gas prediction; genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498643

Filename

5498643

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2844682