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Analysis of drifting dynamics with competing predictors

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

A method for the analysis of nonstationary time series with multiple modes of behaviour is presented. In particular, it is not only possible to detect a switching of dynamics but also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm for segmenting the data according to the modes and a subsequent search through the space of possible drifts. Applications to speech and physiological data demonstrate that analysis and modeling of real world time series can be improved when the drift paradigm is taken into account.

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References

  1. Cacciatore, T.W., Nowlan, S.J. (1994). Mixtures of Controllers for Jump Linear and Non-linear Plants. NIPS'93, Morgan Kaufmann.

    Google Scholar 

  2. Kaneko, K. (1989). Chaotic but Regular Posi-Nega Switch among Coded Attractors by Cluster-Size Variation, Phys. Rev. Lett. 63, 219.

    Google Scholar 

  3. Kohlmorgen, J., Müller, K.-R., Pawelzik, K. (1994). Competing Predictors Segment and Identify Switching Dynamics. ICANN'94, Springer London, 1045–1048.

    Google Scholar 

  4. Kohlmorgen, J., Müller, K.-R., Pawelzik, K. (1995). Improving short-term prediction with competing experts. ICANN'95, EC2 & Cie, Paris, 2:215–220.

    Google Scholar 

  5. Langhorst, P., Schulz, B., Schulz, G., Lambertz, M. (1983). Reticular formation of the lower brainstem. A common system for cardiorespiratory and somatomotor functions: discharge patterns of neighbouring neurons influenced by cardiovascular and respiratory afferents. J.A.N.S. 9: 411–432.

    Google Scholar 

  6. Lapedes, A., R. Farber (1987). Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico.

    Google Scholar 

  7. Mackey, M., Glass, L. (1977). Oscillation and Chaos in a Physiological Control System, Science 197, 287.

    Google Scholar 

  8. Müller, K.-R., Kohlmorgen, J., Pawelzik, K. (1995). Analysis of Switching Dynamics with Competing Neural Networks, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E78-A, No.10, 1306–1315.

    Google Scholar 

  9. Müller, K.-R., Kohlmorgen, J., Rittweger, J., Pawelzik, K. (1995). Analysing Physiological Data from the Wake-Sleep State Transition with Competing Predictors, in NOLTA 95: Las Vegas Symposium on Nonlinear Theory and its Applications.

    Google Scholar 

  10. Müller, K.-R., Kohlmorgen, J., Rittweger, J., Pawelzik, K., in preparation

    Google Scholar 

  11. Packard, N.H., Crutchfield J.P., Farmer, J.D., Shaw, R.S. (1980). Geometry from a Time Series. Physical Review Letters, 45:712–716, 1980.

    Google Scholar 

  12. Pawelzik, K. (1994). Detecting coherence in neuronal data. In: Domany,E., Van Hemmen, L., Schulten, K., (Eds.), Physics of neural networks, Springer.

    Google Scholar 

  13. Pawelzik, K., Kohlmorgen, J., Müller, K.-R. (1996). Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics, Neural Computation, 8:2, 342–358.

    Google Scholar 

  14. Rabiner, L.R. (1988). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, Vol 77, pp. 257–286.

    Google Scholar 

  15. Takens, F. (1981). Detecting Strange Attractors in Turbulence. In: Rand, D., Young, L.-S., (Eds.), Dynamical Systems and Turbulence, Springer Lecture Notes in Mathematics, 898, 366.

    Google Scholar 

  16. Weigend, A.S., Mangeas, M. (1995). Nonlinear gated experts for time series: discovering regimes and avoiding overfitting, University of Colorado, Computer Science Technical Report, CU-CS-764-95.

    Google Scholar 

  17. Weigend, A.S., Gershenfeld, N.A. (Eds.) (1994). Time Series Prediction: Forecasting the Future and Understanding the Past, Addison-Wesley.

    Google Scholar 

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Author information

Authors and Affiliations

  1. GMD FIRST, Rudower Chaussee 5, 12489, Berlin, Germany

    J. Kohlmorgen & K. -R. Müller

  2. Inst. f. theo. Physik, Universität Frankfurt, 60054, Frankfurt/M., Germany

    K. Pawelzik

Authors
  1. J. Kohlmorgen
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  2. K. -R. Müller
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  3. K. Pawelzik
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Kohlmorgen, J., Müller, K.R., Pawelzik, K. (1996). Analysis of drifting dynamics with competing predictors. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_132

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  • DOI: https://doi.org/10.1007/3-540-61510-5_132

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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