J. Abella, A. Gonzalez, J. Llosa, and X. Vera, Near-optimal loop tiling by means of cache miss equations and genetic algorithms, Proceedings. International Conference on Parallel Processing Workshop, 2001.
DOI : 10.1109/ICPPW.2002.1039779

P. Koiran, M. Cosnard, and M. H. Garzon, Computability with low-dimensional dynamical systems, Theoretical Computer Science, vol.132, issue.1-2, p.113, 1994.
DOI : 10.1016/0304-3975(94)90229-1
URL : http://doi.org/10.1016/0304-3975(94)90229-1

H. Siegelmann and E. Sontag, Turing computability with neural nets, Applied Mathematics Letters, vol.4, issue.6, p.77, 1991.
DOI : 10.1016/0893-9659(91)90080-F
URL : http://doi.org/10.1016/0893-9659(91)90080-f

J. Síma and P. Orponen, Continuous-Time Symmetric Hopfield Nets Are Computationally Universal, Neural Computation, vol.10, issue.3, p.693, 2003.
DOI : 10.1145/273865.273914

R. Hegger, H. Kantz, and T. Schreiber, package, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.9, issue.2, p.413, 1999.
DOI : 10.1063/1.166424

Y. Yamamoto, Modern techniques in neuroscience research, chap. Detection of chaos and fractals from experimental time series, pp.669-687, 1999.

C. Peng, S. Havlin, H. Stanley, and A. Goldberger, Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.5, issue.1, p.82, 1995.
DOI : 10.1063/1.166141

G. Rangarajan and M. Ding, Integrated approach to the assessment of long range correlation in time series data, Physical Review E, vol.61, issue.5, p.4991, 2000.
DOI : 10.1103/PhysRevE.61.4991

F. Takens, Detecting strange attractors in turbulence, Dynamical Systems and Turbulence, pp.366-381, 1981.
DOI : 10.1007/BF01646553

N. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, Geometry from a Time Series, Physical Review Letters, vol.45, issue.9, p.712, 1980.
DOI : 10.1103/PhysRevLett.45.712

M. Casdagli, S. Eubank, J. D. Farmer, and J. Gibson, State space reconstruction in the presence of noise, Physica D: Nonlinear Phenomena, vol.51, issue.1-3, p.52, 1991.
DOI : 10.1016/0167-2789(91)90222-U

M. Rosenstein, J. Collins, and C. D. Luca, A practical method for calculating largest Lyapunov exponents from small data sets, Physica D: Nonlinear Phenomena, vol.65, issue.1-2, p.117, 1993.
DOI : 10.1016/0167-2789(93)90009-P
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.5860

H. Kantz and T. Schreiber, Nonlinear time series analysis, 1996.
DOI : 10.1017/CBO9780511755798

M. Kennel, R. Brown, and H. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A, vol.45, issue.6, p.3403, 1992.
DOI : 10.1103/PhysRevA.45.3403

J. Zbilut, N. Thomasson, and C. Webber, Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals, Medical Engineering & Physics, vol.24, issue.1, p.53, 2002.
DOI : 10.1016/S1350-4533(01)00112-6

M. Thiel, M. Romano, J. Kurths, and P. Read, Estimation of dynamical invariants without embedding by recurrence plots, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.14, issue.2, p.234, 2004.
DOI : 10.1063/1.1667633

J. Theiler, Estimating fractal dimension, Journal of the Optical Society of America A, vol.7, issue.6, p.1055, 1990.
DOI : 10.1364/JOSAA.7.001055

S. V. Buldyrev, A. L. Goldberger, S. Havlin, R. N. Mantegna, M. E. Matsa et al., Long-range correlation properties of coding and noncoding DNA sequences: GenBank analysis, Physical Review E, vol.51, issue.5, p.5084, 1995.
DOI : 10.1103/PhysRevE.51.5084

H. D. Abarbanel, R. Brown, and M. B. , Variation of Lyapunov exponents on a strange attractor, Journal of Nonlinear Science, vol.156, issue.2, p.175, 1991.
DOI : 10.1007/BF01209065

A. Wolf, J. Swift, H. Swinney, and J. Vastano, Determining Lyapunov exponents from a time series, Physica D: Nonlinear Phenomena, vol.16, issue.3, p.285, 1985.
DOI : 10.1016/0167-2789(85)90011-9

T. Sherwood, E. Perelman, G. Hamerly, B. Calder, and . Oper, Automatically characterizing large scale program behavior, ACM SIGOPS Operating Systems Review, vol.36, issue.5, p.45, 2002.
DOI : 10.1145/635508.605403
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.6150