Title :
Coal Gas Concentration Predication Based on Chaotic Time Series
Author_Institution :
Coll. of Electr. & Control Eng., Xi´an Univ. of Sci. & Technol., Xi´an, China
Abstract :
A novel coal gas concentration predication model is introduces based on the chaotic time series theory in this paper. According to the Takens theorem, the gas concentration phase space is reconstructed, the embedded dimension m and the time delay τ are calculated by C-C algorithm, the Lyapunov exponent λ is solved with wolf method, and the time series neural network prediction model is established. Research results show that the gas concentration time series has a chaotic characteristic when the Lyapunov exponent λ is 0.2392. While the embedded dimension m and the time delay τ are 6, respectively, the original gas concentration changes can be restored with the gas concentration reconstruction in sequence. Therefore the coal gas concentration predication model is feasible to predict gas concentration change in short time.
Keywords :
Lyapunov methods; coal; natural gas technology; neural nets; production engineering computing; time series; Lyapunov exponent; Takens theorem; coal gas concentration prediction model; gas phase space reconstruction; time series neural network prediction model; Automation; Chaos; Control engineering; Data mining; Delay effects; Educational institutions; Neural networks; Predictive models; Production; Space technology; Chaotic Time Series; Coal Gas; Concentration Predication; Phase Space Reconstruction;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
DOI :
10.1109/ICICTA.2010.361
