Information entropy is applied to the analysis of time series generated by dynamical systems. Complexity of a temporal or spatio-temporal signal is defined as the difference between the sum of entropies of the local linear regions of the trajectory manifold, and the entropy of the globally linearized manifold. When the entropies are Tsallis entropies, the complexity is characterized by the value of q. (C) 2004 Elsevier B.V. All rights reserved.2nd Sardinian International Conference on News and Expectations in Thermostatistics, Sep 21-28, 2003, Villasimius, Ital
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract—In times past the term signal-analysis was synonymous with spectral analysis. There is howe...
This paper addresses the problem of measuring complexity from embedded attractors as a way to charac...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
Both entropy and complexity are central concepts for the understanding and development of Informatio...
By calculating the conditional entropy of two different chaotic time series, converted into symbolic...
In this chapter we aim at presenting applications of notions from Information Theory to the study of...
This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and ...
We introduce a class of information measures based on group entropies, allowing us to describe the i...
: A technique for identification and quantification of chaotic dynamics in experimental time series ...
The search for patterns in time series is a very common task when dealing with complex systems. This...
We introduce a class of information measures based on group entropies, allowing us to describe the i...
Information theory and entropy measures have been extensively applied in ecology in different areas ...
Time series from chaotic and stochastic systems shape properties which can make it hard to distingui...
Measuring the complexity of dynamical systems is important in order to classify them and better unde...
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract—In times past the term signal-analysis was synonymous with spectral analysis. There is howe...
This paper addresses the problem of measuring complexity from embedded attractors as a way to charac...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
Both entropy and complexity are central concepts for the understanding and development of Informatio...
By calculating the conditional entropy of two different chaotic time series, converted into symbolic...
In this chapter we aim at presenting applications of notions from Information Theory to the study of...
This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and ...
We introduce a class of information measures based on group entropies, allowing us to describe the i...
: A technique for identification and quantification of chaotic dynamics in experimental time series ...
The search for patterns in time series is a very common task when dealing with complex systems. This...
We introduce a class of information measures based on group entropies, allowing us to describe the i...
Information theory and entropy measures have been extensively applied in ecology in different areas ...
Time series from chaotic and stochastic systems shape properties which can make it hard to distingui...
Measuring the complexity of dynamical systems is important in order to classify them and better unde...
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract—In times past the term signal-analysis was synonymous with spectral analysis. There is howe...
This paper addresses the problem of measuring complexity from embedded attractors as a way to charac...