Add to ORKGIn an attempt to quantify the dynamical complexity of power systems, we introduce the use of a non-linear time series technique to detect complex dynamics in a signal. The technique is a significant reinterpretation of the Approximate Entropy (ApEn) introduced by Pincus, as an approximation to the Eckmann- Ruelle entropy. It is examined in the context of power systems, and several examples are explored
The search for patterns in time series is a very common task when dealing with complex systems. This...
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract The complex behavior of many systems in nature requires the application of robust methodolo...
(Communicated by the associate editor name) Abstract. We propose an entropy statistic designed to as...
Natural time is a new time domain introduced in 2001. The analysis of time series associated with a ...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
A system’s response to disturbances in an internal or external driving signal can be characterized ...
Measurement of time series complexity and predictability is sometimes the cornerstone for proposing ...
The Markov and non-Markov processes in complex systems are examined with the help of dynamical infor...
In this paper we study several natural and man-made complex phenomena in the perspective of dynamica...
Complexity may be one of the most important measurements for analysing time series data; it covers o...
Real systems are often complex, nonlinear, and noisy in various fields, including mathematics, natur...
Approximate Entropy and especially Sample Entropy are recently frequently used algorithms for calcul...
We analyze the principle of entropy increment for analysis of the stability of operation of technica...
Perhaps the single most important lesson to be drawn from the study of non-linear dynamical sys-tems...
The search for patterns in time series is a very common task when dealing with complex systems. This...
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract The complex behavior of many systems in nature requires the application of robust methodolo...
(Communicated by the associate editor name) Abstract. We propose an entropy statistic designed to as...
Natural time is a new time domain introduced in 2001. The analysis of time series associated with a ...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
A system’s response to disturbances in an internal or external driving signal can be characterized ...
Measurement of time series complexity and predictability is sometimes the cornerstone for proposing ...
The Markov and non-Markov processes in complex systems are examined with the help of dynamical infor...
In this paper we study several natural and man-made complex phenomena in the perspective of dynamica...
Complexity may be one of the most important measurements for analysing time series data; it covers o...
Real systems are often complex, nonlinear, and noisy in various fields, including mathematics, natur...
Approximate Entropy and especially Sample Entropy are recently frequently used algorithms for calcul...
We analyze the principle of entropy increment for analysis of the stability of operation of technica...
Perhaps the single most important lesson to be drawn from the study of non-linear dynamical sys-tems...
The search for patterns in time series is a very common task when dealing with complex systems. This...
Measures of entropy have been widely used to characterize complexity, particularly in physiological ...
Abstract The complex behavior of many systems in nature requires the application of robust methodolo...