Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg-Marquardt learning algorithm

Chaos, Solitons and Fractals

Volumn 41, Issue 4, 2009, Pages 1975-1979

  Mirzaee, Hossein ^a  

^a RESEARCH INSTITUTE OF FOOD SCIENCE AND TECHNOLOGY RIFST   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; NEURAL NETWORKS; TIME SERIES;

CHAOTIC TIME SERIES; HIDDEN LAYERS; LEVENBERG-MARQUARDT LEARNING ALGORITHMS; LONG-TERM PREDICTION; MACKEY-GLASS TIME SERIES; MULTI-LAYER PERCEPTION; MULTI-STEP PREDICTION; REGRESSION VECTORS;

LEARNING ALGORITHMS;

EID: 67349200040     PISSN: 09600779     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chaos.2008.08.016     Document Type: Article

References (18)

1. Nelles O. Nonlinear system identification (2001), Berlin Heidelberg, New York
2. Haykin S. Neural networks: a comprehensive foundation (1994), Macmillan, New York
3. Cichoki A., and Chichester R. Neural networks for optimization and signal processing (1993), Wiley, New York
4. Bossley KM. Neurofuzzy modelling approaches in system identification. Ph.D. thesis, University of Southampton, Southampton; 1997.
5. Park J., and Sandberg I.W. Approximation and radial basis function networks. Neural Comput 5 (1993) 305-316
6. Cannas B., and Cincotti S. Neural reconstruction of Lorenz attractors by an observable. Chaos Solitons Fractals 14 (2002) 81-86
7. Erfanian Omidvar A. Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction. Chaos Solitons Fractals 22 (2004) 757-766
8. Pan S.-T., and Lai C.-C. Identification of chaotic systems by neural network with hybrid learning algorithm. Chaos Solitons Fractals 37 (2008) 233-244
9. Leung H., Lo T., and Wang S. Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans Neural Network 12 5 (2001) 1163-1172
10. Sharifi J., Araabi B.N., and Lucas C. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling. Earth Planets Space 58 (2006) 331-341
11. Sharifi J., Lucas C., and Araabi B.N. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions. Space Weather 4 (2006) 1-12
12. Farmer J.D., and Sidorowich J.J. Predicting chaotic time series. Phys Rev Lett 59 8 (1987) 845-848
13. Cechin A.L., Pechmann D.R., and de Oliveira L.P.L. Optimizing Markovian modeling of chaotic systems with recurrent neural networks. Chaos Solitons Fractals 37 (2008) 1317-1327
14. Lin T., Horne B.G., Tino P., and Giles C.L. Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans Neural Network 1 6 (1996)
15. Hornik K. Approximation capabilities of multilayer feedforward neural networks. Neural Networks 4 2 (1991) 251-257
16. Hagan M.T., and Menhaj M. Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Network 5 6 (1999) 989-993
17. Hagan M.T., Demuth H.B., and Beale M.H. Neural network design (1996), PWS Publishing, Boston, MA
18. Mackey M., and Glass L. Oscillation and chaos in physiological control systems. Science 197 (1977) 287