ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

Authors

  • Vladimir Golovko

DOI:

https://doi.org/10.47839/ijc.2.2.201

Keywords:

Lyapunov spectrum, Multilayer Neural Networks, Chaotic processes, Dynamical system

Abstract

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

References

I. Prigogine. The End of Certainty: Time, Chaos, and the New Laws of Nature. The Free Press, N.Y., 1997, 240p.

H. Kantz, T. Schreiber. Nonlinear time series analysis. Cambridge, Cambridge University Press, 1997.

V. Golovko. From Neural Networks to Intelligent Systems: Selected Aspects of Training. Application and Evolution // chapter of NATO book Limitations and Future Trends in Neural Computation. - Amsterdam: IOS Press, 2003. - 25 p.

V. Golovko, Y. Savitsky, N. Maniakov. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing // chapter of NATO book Neural networks for instrumentation, measurement and related industrial applications. - Amsterdam: IOS Press, 2003, pp. 119-143.

F. Takens. Detecting strange attractors in turbulence, Lecture Notes in Mathematics. Vol. 898, Springer-Verlag, Berlin, 1980, pp. 366-381; and in Dynamical System in Turbulence, Warlock, 1980, eds. D. Rand and L.S. Young.

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Published

2014-08-01

How to Cite

Golovko, V. (2014). ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS. International Journal of Computing, 2(2), 30-34. https://doi.org/10.47839/ijc.2.2.201

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Articles