The gas concentration forecast based on RBF neural network and chaotic sequence

Author

GuangHua Yu ; Liqin Shi

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Heihe Univ., Heihe, China

fYear

2013

fDate

23-24 Dec. 2013

Firstpage

745

Lastpage

748

Abstract

For getting the accurately coal gas concentration, according to the its nonlinear characteristics and time series chaotic characteristics, established a forecasting model, using chaos theory and RBF neural network. To get the training samples, it reconstructed gas concentration time series. Using MATLAB simulation to forecast analysis, the result shows that the relative prediction error is from 0 to 3%, and the mean square error is 0.0056. The result is well, and the examples show prediction model is feasible.

Keywords

chaos; forecasting theory; fossil fuels; mining industry; radial basis function networks; time series; MATLAB simulation; RBF neural network; chaos theory; chaotic sequence; coal gas concentration; forecasting model; gas concentration forecast; mean square error; radial basis function neural network; relative prediction error; time series chaotic characteristics; Chaos; Delays; Educational institutions; Mutual information; Neural networks; Time series analysis; Training; Chaotic time series; Gas concentration; Neural network; Phase space reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2013 2nd International Symposium on

Conference_Location

Toronto, ON

Type

conf

DOI

10.1109/IMSNA.2013.6743384

Filename

6743384