Chaotic Time Series Adaptive Prediction Based on Volterra Series

Fang Dai; Gao Hua Liao

doi:10.4028/www.scientific.net/AMR.945-949.2495

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Chaotic Time Series Adaptive Prediction Based on...

Chaotic Time Series Adaptive Prediction Based on Volterra Series

Abstract:

At present, the mine has only realized the real-time monitoring of gas, but not the prediction of gas.There were some limitation of the traditional prediction method, such as modeling subjectivism and statistical prediction. Because it can dynamically adjust the parameters of the model, adaptive prediction method can get the current time according to the prediction error of data and the current time, real-time fault prediction model parameters, this is a very consistent with the prediction method for practical use.This paper presents the gas emission chaos time series method by using volterra series prediction, and on the basis to establish time-series prediction models. The results show that the method not only avoids the phase space reconstruction, but also avoid the points in the neighborhood search, in real-time, with very high efficiency.

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Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

2495-2498

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.945-949.2495

Citation:

Cite this paper

Online since:

June 2014

Authors:

Fang Dai*, Gao Hua Liao

Keywords:

Adaptive Prediction, Chaotic Time Series , Gas Emission

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