Abstract
This summary describes some of my work on construction of dynamical system models from data, as part of a larger project to identify nonlinear dynamics and distinguish it from noise. In the space available it is only possible to look briefly at a number of different ideas and applications. The reader is referred to the bibliography for fuller details.
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References
A. Bowyer, Computing Dirichlet tesselations, The Computer Journal, 24 (2), 162–166 (1981).
M. Casdagli, Phys. Rev. Letters, to appear (1989).
J.D. Farmer and J.J. Sidorowich, Predicting chaotic time series, Phys. Rev. Letters 59 (8), 845–848 (1987).
J.H. Friedman, Multivariate adaptive regression splines, Technical Report 102, Laboratory for Computational Statistics, Stanford University (1988).
A.I. Mees, Modelling Complex Systems, Proceedings of the Conference on Modelling Complex Systems, eds. L.S. Jennings, A.I. Mees and T.L. Vincent, Birkhauser-Boston (1989).
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© 1989 Plenum Press, New York
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Mees, A. (1989). Modelling Dynamical Systems from Real-World Data. In: Abraham, N.B., Albano, A.M., Passamante, A., Rapp, P.E. (eds) Measures of Complexity and Chaos. NATO ASI Series, vol 208. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-0623-9_49
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DOI: https://doi.org/10.1007/978-1-4757-0623-9_49
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