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Tytuł artykułu

Classification of the signals presenting different representations of the same process

Identyfikatory
Warianty tytułu
PL
Klasyfikacja sygnałów zastosowanych w różnych procesach
Języki publikacji
EN
Abstrakty
EN
In this paper are presented results of applying the methods of the time series analysis to the problem of recognizing small boats. it has been showed that the acoustic signals of the boats can be classified by means of clustering algorithms.
PL
Artykuł prezentuje rezultaty zastosowania metod analizy szeregów czasowych do rozwiązywania problemu rozpoznawania małych łodzi. Wykazano, że sygnał hydroakustyczny generowany przez łodzie może być klasyfikowany przy zastosowaniu algorytmu klasteryzacyjnego.
Rocznik
Strony
227--238
Opis fizyczny
Bibliogr. 48 poz., wykr.
Twórcy
  • The Naval University of Gdynia (81-919 Gdynia, Śmidowicza Str. 69)
autor
  • The Naval University of Gdynia (81-919 Gdynia, Śmidowicza Str. 69)
Bibliografia
  • [1] H. D. I. Abarbanel, Analysis of observed chaotic data, Springer, New York, 1996.
  • [2] H. D. I. Abarbanel, R. Brown, J. J. Sidorowich, L. Sh. Tsimring, The analysis of observed chaotic data in physical systems, Phys. Rev, A 38, 3017, 1988.
  • [3] H. D. I. Abarbanel, R. Brown, J. J. Sidorowich, L. Sh. Tsimring, The analysis of obsented chaotic data in physical systems, Rev. Mod. Phys., 65, 1331, 1993.
  • [4] A. M. Albano, J. Muench, C. Schwant, A. I. Mees, P. E. Rapp, Singular-value decomposition and the grassberger-procaccia algorithm, Phys. Rev, A 38, 3017, 1988.
  • [5] T. Buzug, G. Pfister, Comparison of algorithms calculating optimal parameters for delay time coordinates, Physica, D 58, 127, 1992.
  • [6] M. Casdagli, S. Eubank, J. D. Farmer, J. Gibson, State space reconstruction in the presence of noise, Physica, D 51, 52, 1991.
  • [7] C. Diks, J. C. van Houwelingen, F. Takens, J. DeGoede, Reversibility as a criterion for discriminating time series, Phys. Lett., A 201, 221, 1995.
  • [8] C. Diks, W. R. van Zwet, F. Takens, J. DeGoede, Detecting differences between delay vector distributions, Phys. Rev., E 53, 2169, 1996.
  • [9] J. P. Eckmann, D. Ruelle, Ergodic theory of chaos and strange attractors, Rev. Mod. Phys., 57,617, 1985.
  • [10] K. Fukunaga, Introduction to Statistical Patem Recognition, Academic Press, New York, 1990.
  • [11] P. Grassberger, R. Hegger, H. Kantz, C. Schaffrath, T. Schreiber, On noise reduction methods for chaotic data, CHAOS, 3, 127, 1993.
  • [12] P. Grassberger, T. Schreiber, C. Schaffrath, Nonlinear time seąuence analysis, Int. J. Bifurcation and Chaos, 1, 521, 1991.
  • [13] R. Hegger, H. Kantz, T. Schreiber, Practical implementation of nonlinear time series methods: The tisean package, CHAOS, 9, 413, 1999.
  • [14] F. Takens in D. A. Rand, L.-S. Young eds. Dynamical systems, and turbulence. Detecting strange attractors in turbulence, Lecture notes in mathematics, Vol. 898, Springer, New York, 1981.
  • [15] L. Jaeger, H. Kantz, Unbiased reconstruction underlying a noisy chaotic time series, CHAOS, 6,440, 1996.
  • [16] I. T. Jolliffe, Principal component analysis, Springer, New York, 1986.
  • [17] J. Kadtke, Classification of highly noisy signals using global dynamical models, Phys. Lett., A 203, 196, 1995.
  • [18] H. Kantz, Quantifying the closeness of fractal measures, Phys. Rev., E 49, 5091, 1994.
  • [19] H. Kantz, T. Schreiber, Nonlinear time series analysis, Cambridge University Press, Oxford, 1997.
  • [20] H. Kantz, T. Schreiber, 1. Hoffmann, T. Buzug, G. Pfister, L. G. Flepp, J. Simonet, R. Badii, E. Brun, Nonlinear noise reduction: A case study on experimental data, Phys. Rev., E 48, 1529, 1993.
  • [21] D. Kapłan, L. Glass, Understanding nonlinear dynamics, Springer, New York, 1995.
  • [22] L. Kaufman, P. J. Rousseeuw, Finding Groups in Data, an introduction to cluster analysis, Wiley, New York, 1990.
  • [23] E. J. Kostelich, T. Schreiber, Noise reduction in chaotic time series data: A survey of common methods, Phys. Rev., E 48, 1752, 1993.
  • [24] E. J. Kostelich, J. A. Yorke, Noise reduction in dynamical systems, Phys. Rev., A 38, 1649, 1988.
  • [25] D. Kugiumtzis, State space reconstruction parameters in the analysis of chaotic time series the role ofthe time window length, Physica, D 96, 13, 1996.
  • [26] D. Kugiumtzis, Assessing different norms in nonlinear analysis of noisy time series, Physica, D105,62,1997.
  • [27] W. Liebert, H. G. Schuster, Proper choice of the time delays for the analysis of chaotic time series, Phys. Lett., A 142, 107, 1989.
  • [28] R. Moeckel, B. Murray, Measuring the distance between time series Physica, D 102, 187, 1997.
  • [29] E. Ott, T. Sauer, J. A. Yorke, Coping with chaos, Wiley, New York, 1994.
  • [30] D. B. Percival, A. T. Walden, Spectral Analysis For Physical Applications, Cambridge University Press, Cambridge, 1993.
  • [31] M. B. Priestley, Non-linear and non-stationary time series analysis, Academic Press, London, 1988.
  • [32] P. E. Rapp, A. M. Albano, T. I. Schmah, L. A. Farwell, Filtered noise can mimie low-dimensional chaotic attractors, Phys. Rev., E 47, 2289, 1993.
  • [33] T. Sauer, J. Yorke, How many delay coordinates do you need? Int. J. Bifurcation and Chaos, 3, 737, 1993.
  • [34] T. Sauer, J. Yorke, M. Casdagli, Embedology, J. Stat. Phys., 65, 579, 1991.
  • [35] T. Schreiber, Detecting anad analysing non-stationarity in a time series using nonlinear cross predictions, Phys. Rev. Lett., 78, 843, 1997.
  • [36] T. Schreiber, Constrained randomization of time series data, Phys. Rev. Lett., 80, 2105, 1998.
  • [37] T. Schreiber, Interdisciplinary applications of nonlinear time series methods, Phys. Reports, 1, 1999.
  • [38] T. Schreiber, P. Grassberger, A simple noise-reduction method for real data, Phys. Lett., A 160,411, 1991.
  • [39] T. Schreiber, H. Kantz in Y. Kravtsov, J. Kadtke eds., Observing andpredicting chaotic signals: Is 2% noise too much? in Predictability of complex dynamical systems, Springer, New York, 1996.
  • [40] T. Schreiber, H. Kantz, Noise in chaotic data: Diagnosis and treatment, CHAOS, 5, 133, 1995.
  • [41] T. Schreiber, A. Schmitz, lmmproved surrogate data for nonlinearity tests, Phys. Rev. Lett., Tl, 635, 1996.
  • [42] T. Schreiber, A. Schmitz, Classification of time series data with nonlinear simmilarity measures, Phys. Rev. Lett., 79, 1475, 1997.
  • [43] T. Schreiber, A. Schmitz, Discrimination power of measures for nonlinearity in a time series, Phys. Rev., E 55, 5443, 1997.
  • [44] J. Theiler, D. Prichard, Generating surrogate data for time series with several simultaneously measured variables, Phys. Rev. Lett., 73, 951, 1994.
  • [45] J. Theiler, D. Prichard, Constrained-realization monte-carlo method for hypoothesis testing, Physica, D 94, 221, 1996.
  • [46] H. Tong, Non-linear time series analysis, Oxford University Press, Oxford, 1990.
  • [47] A. S. Weigend, N. A. Gershenfeld, Time series prediction: Forecasting the futurę and understanding the past, Santa Fe Institute Studies in the Science of Complexity, Proc. Vol. XV, Adsison-Wesley, Reading, MA, 1993.
  • [48] http://www.mpipks-dresden.mpg.de7tisean
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ2-0017-0055
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