Forecast chaotic time series data by DBNs

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

Download Full-text

Constructing bifurcation diagram for a chaotic time-series data through a recurrent neural network model

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. ◽

10.1109/iconip.2002.1201915 ◽

2002 ◽

Cited By ~ 2

Author(s):

J. Krishnaiah ◽

C.S. Kumar ◽

M.A. Faruqi

Keyword(s):

Neural Network ◽

Time Series ◽

Bifurcation Diagram ◽

Network Model ◽

Recurrent Neural Network ◽

Neural Network Model ◽

Time Series Data ◽

Chaotic Time Series ◽

Series Data

Download Full-text

Topological analysis of chaotic time series data from the Belousov-Zhabotinskii reaction

Journal of Nonlinear Science ◽

10.1007/bf01209064 ◽

1991 ◽

Vol 1 (2) ◽

pp. 147-173 ◽

Cited By ~ 105

Author(s):

G. B. Mindlin ◽

H. G. Solari ◽

M. A. Natiello ◽

R. Gilmore ◽

X. -J. Hou

Keyword(s):

Time Series ◽

Time Series Data ◽

Topological Analysis ◽

Chaotic Time Series ◽

Series Data ◽

Belousov Zhabotinskii Reaction

Download Full-text

Chaotic Characteristics of Workface Gas Emission Time-Series Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.340.456 ◽

2013 ◽

Vol 340 ◽

pp. 456-460 ◽

Cited By ~ 1

Author(s):

Mei Ying Qiao ◽

Jian Yi Lan

Keyword(s):

Time Series ◽

Time Series Data ◽

Chaotic Behavior ◽

Chaotic Time Series ◽

Point Of View ◽

Series Data ◽

Embedding Dimension ◽

Minimum Entropy ◽

Gas Emission ◽

Emission Time

The chaotic time series phase space reconstruction theory based in this paper. First, the appropriate embedding dimension and delay time are selected by minimum entropy rate. Followed the chaotic behavior are analyzed by the use of the Poincare section map and Power spectrum of time series from the qualitative point of view. Based on NLSR LLE the quantitative study of the chaotic time series characteristics indicators is proposed. Finally, the gas emission workface of Hebi 10th Mine Coal is studied. The several analytical results of the above methods show that: the gas emission time-series data of this workface has chaotic characteristics.

Download Full-text