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Bibliography Bibliography [1] Henry Abarbanel". Local false nearest neighbors and dynamical dimensions from observed chaotic data. Physical Review E, 47(5):3057{3068, 1993. [2] Henry Abarbanel. Analysis of observed chaotic data. Springer, New York, 1996. [3] Henry Abarbanel. Analysis of observed chaotic data. Institute for nonlinear science. Springer, 1996. [4] Jonathan B.Dingwell. Lyapunov Exponents. John Wiley and Sons, Inc., 2006. [5] J. Brocker, U. Parlitz, and M. Ogorzalek. Nonlinear noise reduction. Proceedings of the IEEE, 90(5):898{918, 2002. [6] David S Broomhead and Gregory P King. Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2):217{236, 1986. [7] Liangyue Cao. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110(1):43{50, 1997. [8] Liangyue Cao, Alistair Mees, and Kevin Judd. Dynamics from multivariate time series. Physica D: Nonlinear Phenomena, 121(1):75{88, 1998. [9] James R Carey. Demography and population dynamics of the mediterranean fruit fly. Ecological Modelling, 16(2-4):125{150, 1982. [10] Martin Casdagli, Tim Sauer, and James A Yorke. Embedology. Journal of Statistical Physics, 65(3/4):579{616, 1991. [11] A. Chatterjee, J. P. Cusumano, and D. Chelidze. Optimal tracking of parameter drift in a chaotic system: Experiment and theory. Journal of Sound and Vibration, 250(5):877{901, 2002. [12] Anindya Chatterjee. An introduction to the proper orthogonal decomposition. Current Science: Computational Science, 78(7):808{817, 2000. 205 206 BIBLIOGRAPHY [13] C Chatzinakos and C Tsouros. Estimation of the dimension of chaotic dynamical systems using neural networks and robust location estimate. Simulation Modelling Practice and Theory, 51:149{156, 2015. [14] D. Chelidze and M. Liu. Reconstructing slow-time dynamics from fast-time measurements. Philosophical Transaction of the Royal Society A, 366:729{3087, 2008. [15] D. Chelidze and W. Zhou. Smooth orthogonal decomposition-based vibration mode identifica- tion. Journal of Sound and Vibration, 292(3):461{473, 2006. [16] David Chelidze. Statistical analysis of nearest neighbors leads to robust and reliable estimates for minimum embedding dimension. Journal of Computational & Nonlinear Dynamics, (CND- 16-1488), Submitted 2016. [17] David Chelidze and Joseph P. Cusumano. Phase space warping: Nonlinear time series analysis for slowly drifting systems. Philosophical Transactions of the Royal Society A, 364, 2006. [18] David Chelidze, Joseph P. Cusumano, and Anindya Chatterjee. Dynamical Systems Approach to Damage Evalution Tracking, Part I: Desccription and Experimental Application. Journal of Vibration and Acoustics, 124(2):250{257, 2002. [19] David Chelidze and Ming Liu. Dynamical systems approach to fatigue damage identification. Journal of Sound and Vibration, 281:887{904, 2004. [20] David Chelidze and Ming Liu. Multidimensional damage identification based on phase space warping: An experimental study. Nonlinear Dynamics, 46(1{2):887{904, 2006. [21] Antoine Clement and Stephane Laurens. An alternative to the Lyapunov exponent as a damage sensitive feature. Smart Materials and Structures, 20(2), 2011. [22] P Craven and G. Wahba. Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numer. Math., 31:377{403, 1979. [23] JP Cusumano and BW Kimble. A stochastic interrogation method for experimental measure- ments of global dynamics and basin evolution: Application to a two-well oscillator. Nonlinear Dynamics, 8(2):213{235, 1995. [24] JP Cusumano and D-C Lin. Bifurcation and modal interaction in a simplified model of bending- torsion vibrations of the thin elastica. Journal of vibration and acoustics, 117(1):30{42, 1995. [25] Reik V. Donner and Susana M. Barbosa, editors. Nonlinear Time Series Analysis in the Geo- sciences - Applications in Climatology, Geodynamics and Solar-Terrestrial Physics. Berlin: Springer, 2008. BIBLIOGRAPHY 207 [26] James Eells, Hassler Whitney, and Domingo Toledo. Collected papers of Hassler Whitney. Nelson Thornes, 1992. [27] M. EtehadTavakol, E Y K Ng, C. Lucas, S. Sadri, and M. Ataei. Nonlinear analysis using Lyapunov exponents in breast thermograms to identify abnormal lesions. Infrared Physics & Technology, 55(4):345 { 352, 2012. [28] J Doyne Farmer, Edward Ott, and James A Yorke. The dimension of chaotic attractors. Physica D: Nonlinear Phenomena, 7(1-3):153{180, 1983. [29] U. Farooq and BF Feeny. Smooth orthogonal decomposition for modal analysis of randomly excited systems. Journal of Sound and Vibration, 316(1):137{146, 2008. [30] Andrew M Fraser. Reconstructing attractors from scalar time series: a comparison of singular system and redundancy criteria. Physica D: Nonlinear Phenomena, 34(3):391{404, 1989. [31] Andrew M Fraser and Harry L Swinney. Independent coordinates for strange attractors from mutual information. Physical review A, 33(2):1134, 1986. [32] J. Gao, H. Sultan, J. Hu, and W.W. Tung. Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. Signal Processing Letters, IEEE, 17(3):237{240, 2010. [33] S. Ghafari, F. Golnaraghi, and F. Ismail. Effect of localized faults on chaotic vibration of rolling element bearings. Nonlinear Dynamics, 53:287{301, 2008. [34] John F Gibson, J Doyne Farmer, Martin Casdagli, and Stephen Eubank. An analytic approach to practical state space reconstruction. Physica D: Nonlinear Phenomena, 57(1):1{30, 1992. [35] Gene H Golub and Charles F Van Loan. Matrix computations. Johns Hopkins University Press, Baltimore, MD, USA, 1996. [36] Peter Grassberger and Itamar Procaccia. Characterization of strange attractors. Physical review letters, 50(5):346, 1983. [37] Peter Grassberger, Thomas Schreiber, and Carsten Schaffrath. Nonlinear time sequence anal- ysis. International Journal of Bifurcation and Chaos, 1(03):521{547, 1991. [38] Allan Gut. An intermediate course in probability. Springer Texts in Statistics. Springer, New York, NY, 2009. [39] Karen M Hampson and Edward AH Mallen. Chaos in ocular aberration dynamics of the human eye. Biomedical optics express, 3(5):863{877, 2012. 208 BIBLIOGRAPHY [40] Arnold Hanslmeier, Roman Brajˇsa,Jaˇsa Calogovi´c,Bojanˇ Vrˇsnak,Domagoj Ruˇzdjak,Fried- helm Steinhilber, CL MacLeod, Zeljkoˇ Ivezi´c,and Ivica Skoki´c. The chaotic solar cycle-ii. analysis of cosmogenic 10be data. Astronomy & Astrophysics, 550:A6, 2013. [41] Rainer Hegger and Holger Kantz. Improved false nearest neighbor method to detect determinism in time series data. Physical Review E, 60(4):4970, 1999. [42] Rainer Hegger, Holger Kantz, and Thomas Schreiber. Practical implementation of nonlinear time series methods: The tisean package. Chaos: An Interdisciplinary Journal of Nonlinear Science, 9(2):413{435, 1999. [43] Michel H´enon.A two-dimensional mapping with a strange attractor. In The Theory of Chaotic Attractors, pages 94{102. Springer, 1976. [44] Behshad Hosseinifard, Mohammad Hassan Moradi, and Reza Rostami. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from fEEGg signal. Computer Methods and Programs in Biomedicine, 109(3):339 { 345, 2013. [45] Tijana Ivancevic, Lakhmi Jain, John Pattison, and Alex Hariz. Nonlinear dynamics and chaos methods in neurodynamics and complex data analysis. Nonlinear Dynamics, 56(1-2):23{44, 2009. [46] N Jevtic, JS Schweitzer, and P Stine. Optimizing nonlinear projective noise reduction for the detection of planets in mean-motion resonances in transit light curves. CHAOS2010, page 191, 2011. [47] Kevin Judd and Alistair Mees. Embedding as a modeling problem. Physica D: Nonlinear Phenomena, 120(3):273{286, 1998. [48] S Kacimi and S Laurens. The correlation dimension: A robust chaotic feature for classify- ing acoustic emission signals generated in construction materials. Journal of Applied Physics, 106(2), 2009. [49] H. Kantz and T. Schreiber. Nonlinear time series analysis, volume 7. Cambridge University Press, 2003. [50] H. Kantz, T. Schreiber, I. Hoffmann, T. Buzug, G. Pfister, L.G. Flepp, J. Simonet, R. Badii, and E. Brun. Nonlinear noise reduction: A case study on experimental data. Physical Review E, 48(2):1529, 1993. [51] Holger Kantz and Thomas Schreiber. Nonlinear time series analysis. Cambridge University Press, Cambridge, UK, 2 edition, 2004. BIBLIOGRAPHY 209 [52] Holger Kantz and Thomas Schreiber. Nonlinear Time Series Analysis. Cambridge University Press, 2004. [53] Ahmad Kazem, Ebrahim Sharifi, Farookh Khadeer Hussain, Morteza Saberi, and Omar Khadeer Hussain. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 13(2):947{958, 2013. [54] G Kember and AC Fowler. A correlation function for choosing time delays in phase portrait reconstructions. Physics Letters A, 179(2):72{80, 1993. [55] Matthew B Kennel and Henry DI Abarbanel. False neighbors and false strands: A reliable minimum embedding dimension algorithm. Physical review E, 66(2):026209, 2002. [56] Matthew B Kennel, Reggie Brown, and Henry DI Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical review A, 45(6):3403, 1992. [57] Gaetan Kerschen, Jean-Claude Golinval, Alexander F. Vakakis, and Lawrence A. Bergman. The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: An overview. Nonlinear Dynamics, 41:147{169, 2005. [58]H S Kim, R Eykholt,
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