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Optimization Approach to the Estimation and Control of Lyapunov Exponents

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Abstract

In this paper, we describe an algorithm for estimating the Lyapunov exponents from the chaotic dynamics of control systems. Attention is focused on optimization methods for estimating tangent maps from experimental time series data. Our numerical tests show that the algorithm is robust and quite effective, and that its performance is comparable with that of other algorithms. The properties of the algorithm are demonstrated by application to a range of data sets. We consider numerical and experimental data and discuss the computational aspects of the proposed algorithm. New feedback rules for use with optimization techniques in the stimulation of the epileptic brain are proposed.

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References

  1. Iasemidis L., Shiau D., Pardalos P., and Sackellares J., Transition to Epileptic Seizure: An Optimization Approach into Its Dynamics, Discrete Problems with Medical Applications, DIMACS Series, Edited by D. Z. Du, P. Pardalos, and J. Wang, American Mathematical Society, Providence, Rhode Island, Vol. 55, pp. 55–74, 2000.

  2. Kinoshita M., Ikeda A., Matsumoto R., Begum T., Usui K., Yamamoto J., Matsuhashi M., Takayama M., Mikuni N., Takahashi J., Miyamoto S., Shibasaki H., Electric Stimulation on Human Cortex Suppresses Fast Cortical Activity and Epileptic Spikes, Epilepsia, Vol. 45, pp. 787–791, 2004.

  3. V. Yatsenko P. Pardalos C. Sackellares P. Carney O. Prokopyev (2004) Geometric Models, Fiber Bundles, and Biomedical Applications Proceeding of the 5th International Conference on Symmetry in Nonlinear Mathematical Physics Institute of Mathematics Kiev, Ukraine 1518–1525

    Google Scholar 

  4. Pardalos P., Chaovalitwongse W., Iasemidis L., Sackellares J. C., Shiau D., Carney P., Prokopyev O., and Yatsenko V., Seizure Warning Algorithm Based on Optimization and Nonlinear Dynamics, Mathematical Programming, Vol. 101B, pp. 365–385, 2004.

  5. Y.A. Pesin (1998) Dimension Theory in Dynamical Systems: Contemporary Views and Applications Chicago Lectures in Mathematics, University of Chicago Press Chicago, Illinois

    Google Scholar 

  6. Andrzejak R. G., Widman G., Lehnertz K., Rieke C., David P., and Elger C. E., The Epileptic Process as Nonlinear Deterministic Dynamics in a Stochastic Environment: An Evaluation on Mesial Temporal Lobe Epilepsy, Epilepsy Research, Vol. 44, pp. 129–140, 2001.

  7. Wolf A., Swift J., Swinney H. L., and Vastano J. A., Determining Lyapunov Exponents from a Time Series, Physica, Vol. 16D, pp. 285–317, 1985.

  8. Eckmann J. P., and Ruelle D., Ergodic Theory of Chaos and Strange Attractors, Reviews of Modern Physics, Vol. 57, pp. 617–657, 1985.

  9. Sano M., and Sawada Y., Measurement of Lyapunov Spectra from a Chaotic Time Series, Physical Review Letters, Vol. 55, pp. 1082–1085, 1985.

  10. Gaspard P., and G. Nicolis Transport Properties, Lyapunov Exponents, and Entropy per Unit Time, Physical Review Letters, Vol. 65, pp. 1693–1696, 1990.

  11. Iasemidis L., Pardalos P., Sackellares J., Chaovalitwongse W., Carney P., and Shiau D., Can Knowledge of Cortical Site Dynamics in a Preceding Seizure Be Used to Improve Prediction of the Next Seizure? Annals of Neurology, Vol, 52, pp. 65–66, 2002.

  12. Lehnertz K., Andrzejak R., Arnold J., Widman G., Burr W., David P., and Elger C., Possible Clinical and Research Application of Nonlinear EEG Analysis in Humans, Chaos in Brain, Edited by K. Lehnertz, J. Arnold, P. Grassberger, and C. E. Elger, World Scientific, London, UK, pp. 134–155, 2000.

  13. Du N. H., Optimal Control Problem for the Lyapunov Exponents of Random Matrix Products, Journal of Optimization Theory and Application, Vol. 105, pp. 347–369, 2000.

  14. Colonius F., and Kliemann W., The Lyapunov Spectrum of Families of Time-Varying Matrices, Transactions of the American Mathematical Society, Vol. 348, pp. 4389–4408, 1996.

  15. Chen G., and Dong X. On Feedback Control of Chaotic Dynamical Systems, International Journal of Bifurcations and Chaos, Vol. 2, pp. 407–411, 1992.

  16. Battle C., Massana I., and Mirralles A., Lyapunov Exponents for Bilinear Systems, International Journal of Bifurcations and Chaos, Vol. 13, pp. 713–721, 2003.

  17. Pardalos P., Sackellares J., and Yatsenko V., Classical and Quantum Controlled Lattices: Self-Organization, Optimization, and Biomedical Applications, Biocomputing, Edited by P.M. Pardalos, and J. Principe, Kluwer Academic Publishers, Dordrecht, Holland, pp. 199–224, 2002.

  18. R. Kalman S. Falb M. Arbib (1969) Topics in Mathematical System Theory Mc Graw-Hill Company New York, NY

    Google Scholar 

  19. Rosenstein M., Collins J., and De Luca C., A Practical Method for Calculating Largest Lyapunov Exponents from Small Data Sets, Physica, Vol. 65, pp. 117–134, 1993.

  20. Kantz H., A Robust Method to Estimate the Maximal Lyapunov Exponent of a Time Series, Physics Letters, Vol. 185A, pp. 77–87, 1994.

  21. Elger C., and Lehnertz K., Seizure Prediction by Nonlinear Time Series Analysis of Brain Electrical Activity, European Journal of Neuroscience, Vol. 10, pp. 786–789, 1998.

  22. V. V.A. Yatsenko P. Pardalos J. Principe (2003) Cryogenic-Optical Sensor for the Highly Sensitive Gravity Meters Sensors, Systems, and Next-Generation Satellites VI Proceedings of SPIE Orlando, Florida. 549–557

    Google Scholar 

  23. S. Strogatz (1995) Nonlinear Dynamics and Chaos with Application to Physics, Biology, Chemistry, and Engineering Addison-Wesley, Reading Massachusetts

    Google Scholar 

  24. Schiffm S., Jerger, K., Duong D., Chang T., Spano M. and Ditto W., Controlling Chaos in the Brain, Nature, Vol. 370, pp. 615–620, 1994.

  25. Oseledec V., A Multiplicative Ergodic Theorem on the Lyapunov Characteristic Number for a Dynamical System from an Observed Time Series, Transactions of the Moscow Mathematical Society, Vol. 19, pp. 356–362, 1968.

  26. P. Pardalos M. Resende (2002) Handbook of Applied Optimization Oxford University Press Oxford, UK

    Google Scholar 

  27. R. Horst P. Pardalos N. Thoai (2000) Introduction to Global Optimization Nonconvex Optimization and Its Applications, Kluwer Academic Publishers Dordrecht, Holland

    Google Scholar 

  28. Bertsekas D., Incremental Least Squares Methods and the Extended Kalman Filters, SIAM Journal on Optimization, Vol. 6, pp. 807–822, 1996.

  29. Davidon W., New Least Squares Algorithms, Journal of Optimization Theory and Applications, Vol. 18, pp. 187–197, 1976.

  30. Serfaty De Markus A., Detection of the Onset of Numerical Chaotic Instabilities by Lyapunov Exponents, Discrete Dynamics in Nature and Society, Vol. 6, pp. 121–128, 2001.

  31. Gang, H., and Zhilin, Q., Controlling Spatio-Temporal Chaos in Coupled Map Lattice Systems, Physical Review Letters, Vol. 72, pp. 68–71, 1994.

  32. Kocarev L., Tasev Z., and Drittes U., Synchronizing Spatiotemporal Chaos of Partial Differential Equations, Physical Review Letters, Vol. 79, pp. 51–54, 1997.

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This work was supported by NIH, NSF, and CRDF grants.

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Pardalos, P.M., Yatsenko, V.A. Optimization Approach to the Estimation and Control of Lyapunov Exponents. J Optim Theory Appl 128, 29–48 (2006). https://doi.org/10.1007/s10957-005-7554-1

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