Information and entropy in strange attractors
Thesis/Dissertation
·
OSTI ID:5592371
Building on the work of Kolmogorov and Sinai the author uses information-theoretic techniques to measure the deterministic aspects of chaotic time series. The approach taken is novel in that it is intended to be applied to experimental time series. The statistics used are based on mutual information and are thus robust with respect to low levels of measurement noise. Given scalar time series the analysis suggested extracts: the measure theoretic entropy, the measurement accuracy, and parameters for the reconstruction of good phase portraits. An algorithm for implementing the analysis is presented. In the final chapter the analysis techniques developed are compared to classical techniques based on autocorrelation functions, and the new techniques are found to be superior.
- Research Organization:
- Texas Univ., Austin, TX (USA)
- OSTI ID:
- 5592371
- Country of Publication:
- United States
- Language:
- English
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