We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Different Methods of Partitioning the Phase Space of a Dynamic System

by Abir Hadriche, Nawel Jmail, Ridha Elleuch
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 15
Year of Publication: 2014
Authors: Abir Hadriche, Nawel Jmail, Ridha Elleuch
10.5120/16288-5931

Abir Hadriche, Nawel Jmail, Ridha Elleuch . Different Methods of Partitioning the Phase Space of a Dynamic System. International Journal of Computer Applications. 93, 15 ( May 2014), 1-5. DOI=10.5120/16288-5931

@article{ 10.5120/16288-5931,
author = { Abir Hadriche, Nawel Jmail, Ridha Elleuch },
title = { Different Methods of Partitioning the Phase Space of a Dynamic System },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 15 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number15/16288-5931/ },
doi = { 10.5120/16288-5931 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:47.484040+05:30
%A Abir Hadriche
%A Nawel Jmail
%A Ridha Elleuch
%T Different Methods of Partitioning the Phase Space of a Dynamic System
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 15
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the symbolic dynamic, the principal problem to define a symbolic sequence of temporel time series is the use of an appropriate partition of the phase space trajectory data set. In fact, The best way is to estimate the generating partition. However, it is not possible to find generating partitions for most experimental observations because such partitions do not exist when noise is present. In this paper, different partition methods applied for stochastic and chaotic system will be compared in order to choose the coherent one to conserve system dynamical propriety. This partition is called the Markov partition.

References
  1. C. Allefeld, H. Atmanspacher, and J. Wackermann. Mental states as macrostates emerging from brain electrical dynamics. Chaos, 19:015102, 2009.
  2. Erick. M Bollt. Review of chaos communication by feedback control of symbolic dynamics. Bifurcation and Chaos, 13:269–285, 2003.
  3. J. P. Bouchaud and P. Doussal. Numerical study of a ddimensional periodic Lorentz gas with universal properties. Statistical Physics, 41:225–248, 1985.
  4. G. Ciuperca and V. Girardin. On the estimation of the entropy rate of finite Markov chains. Proceedings of the International Symposium on Applied Stochastic Models and Data Analysis, 2005.
  5. J. P. Crutchfield and N. H. Packard. Symbolic dynamics of noisy chaos. Physica D, 7:201–223, 1983.
  6. Pelleg Dan and Moore Andrew. Accelerating exact K-means algorithms with geometric reasoning. In Surajit Chaudhuri and David Madigan, editors, Proceedings of the Fifth International Conference on Knowledge Discovery in Databases, pages 277–281. AAAI Press, aug 1999.
  7. M. Dellnitz and A. Hohmann. A subdivision algorithm for the computation of unstable manifolds and global attractors. Numerische Mathematik, 75:293–317, 1997.
  8. Michael Dellnitz and Oliver Junge. Set oriented numerical methods for dynamical systems, volume 2. Elsevier, 2002.
  9. B. Gaveau and L. S. Shulman. Theory of nonequilibrium firstorder phase transitions for stochastic dynamics. Mathematical Physics, 39:1517–1533, 1997.
  10. B. Gaveau and L. S. Shulman. Dynamical distance: coarse grains, pattern recognition, and network analysis. Sciences Mathematiques, 129:631–642, 2005.
  11. B. Gaveau, L. S. Shulman, and L. J. Shulman. Imaging geometry through dynamics: the observable representation. Physics A, 39:10307–10321, 2006.
  12. J Hadamard. Les surfaces `a courbures oppos´ees et leurs lignes g´eod´esiques. Biological Cybernetics, 43:59–69, 1898.
  13. A. Hadriche, L. Pezard, J. P. Nandrino, H. Ghariani, A. Kachouri, and K. V. Jirsa. Mapping the dynamic repertoire of the resting brain. NeuroImage Journal, 78:448–62, September 2013.
  14. J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Applied Statistics, 28:100–108, 1979.
  15. H. Keller and G. Ochs. Numerical approximation of random attractors. Springer, 1999.
  16. T. Kohonen. Self-organized formation of topologically correct feature maps. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1:281–297, 1982.
  17. Christophe Letellier. Caract´erisation topologique et reconstruction d'attracteurs ´etranges. PhD thesis, Universit´e de Paris, 1994.
  18. Christophe Letellier. syst`emes dynamiques complexes: de la caract´erisation topologique `a la mod´elisation. PhD thesis, 1998.
  19. E. N. Lorenz. Deterministic nonperiodic flow. The Atmospheric Sciences, 20:130–142, 1963.
  20. J. B MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1:281–297, 1967.
  21. Mohd Shukor, Zamzarina, Md. Sap, and Mohd. Noor. Clustering technique in data mining: general and research perspective. Teknologi Maklumat, 14:50–63, 2002.
  22. S. Ulam. A collection of mathematical problems. Interscience Publishers, New York, 1960.
  23. Rajagopalan. Venkatesh, Ray. Asok, Samsi. Rohan, and Mayer. Jeffrey. Pattern identification in dynamical sysytems via symbolic time series analysis. Pattern recognition, 40:97– 07, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Markov Partition Generating Partition Symbolic Sequence Entropy Rate