Neural networks for dynamic modeling

IEEE Signal Processing Magazine

Volumn 14, Issue 6, 1997, Pages 33-35

  Principe, Jose C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; LEARNING SYSTEMS; LYAPUNOV METHODS; MATHEMATICAL MODELS; SIGNAL TO NOISE RATIO; SPURIOUS SIGNAL NOISE; TIME SERIES ANALYSIS;

NONLINEAR AUTOREGRESSIVE MODEL; TIME DELAY EMBEDDING THEOREM;

NEURAL NETWORKS;

EID: 0031269142     PISSN: 10535888     EISSN: None     Source Type: Journal    
DOI: 10.1109/MSP.1997.637300     Document Type: Article

References (16)

1. F. Takens, "On the Numerical Determination of the Dimension of An Attractor," In D. Rand and L.S. Young, editors, Dynamical Systems and Turbulence, Warwick 1980 Lecture Notes in Mathematics, vol. 898, pp. 366-381, Springer-Verlag, 1981.
2. T. Sauer, J.A. Yorke, and M. Casdagli, "Embedology," J. Stat. Phys., vol. 65, pp.579-616, 1991.
3. M. Casdagli, "Nonlinear Prediction of Chaotic Time Series," Physica D 35, pp. 335-356, 1989.
4. A. Lapedes and R. Farber, "Nonlinear Signal Processing using Neural Networks: Prediction and System Modeling," LA-VR-87-2662, Los Alamos National Laboratory, New Mexico, 1987.
5. D. Broomhead, D. Lowe, "Multivariable Functional Interpolation and Adaptive Networks," Complex Systems 2, pp. 321-355, 1988.
6. J.C. Principe, A. Rathie, and J. M. Kuo, "Prediction of Chaotic Time Series with Neural Networks and the Issue of Dynamic Modeling," International Journal of Bifurcation and Chaos., vol. 2, no. 4, pp. 989-996, 1992.
7. Neil Gershenfeld and Andres S. Weigend, "The Future of Time Series," Time Series Prediction: Forecasting the Future and Understanding the Past, A.S. Weigend and N.A. Gershenfeld, eds., 1-70. Addison-Wesley, 1993.
8. S. Haykin, S. Puthusserypady, P. Yee, "Reconstruction of Underlying Dynamics of an Observed Chaotic Process," Comm. Res. Lab. Internal Report, # 353, McMaster Univ., Sept. 1997.
9. Deco G. Schurmann, "Recurrent Neural Networks Capture the Dynamical Invariance of Chaotic Time Series," IEICE Trans. Fund. of Elect. Cowm., and Comp. Sc. 77A, pp. 1840-845, 1994.
10. J.C. Principe, J.M. Kuo, "Dynamic Modeling of Chaotic Time Series with Neural Networks," Adv. Neural Inf. Proc. Syst. 7, (Ed. Tesauro, Toretsky, Leen), pp. 311-318, 1995.
11. I.W. Sandberg and L. Xu, "Uniform Approximation and Gamma Networks," Neural Networks, 10:781-784, 1997.
12. S. Haykin and X. Li, "Detection of Signals in Noise," Proc. of IEEE,
13. S. Roberts and L. Tarassenko, "Automated Sleep EEG Analysis Using an RBF Network," in Applications of Neural Networks, (ed. Murray), Kluwer, 1995.
14. J.M. Kuo, Nonlinear Dynamic Modeling with Artificial Neural Networks, Ph.D. dissertation, University of Florida, 1993.
15. K. Pappas, Phase Space Reconstruction Using Gamma Delay Lines, Master Thesis, University of Florida, 1996.
16. L. Wang, Local Dynamic Modeling with Self-Organising Networks, Ph.D. Dissertation, University of Florida, 1996.