In the first part of the talk I will introduce the concepts of ordinal patterns and statistical complexity and I will show how they can be employed to detect subtle signatures of noise-induced order and complexity in nonlinear systems. I will illustrate the methodology with two paradigmatic models: a Brownian particle in a sinusoidally modulated bistable potential and the FitzHugh-Nagumo model for excitable systems. I will show that resonant-like behavior occurs, in the form of enhanced temporal order, detected as a minimum of Shannon\'s entropy, accompanied by a maximum of the statistical complexity [1]. In the second part of the talk I will discuss how this methodology can be employed for the analysis of experimental data. As a first example I will discuss the analysis of the time-series of the output intensity of a semiconductor laser operating in the feedback-induced low-frequency fluctuations (LFFs) regime; a second example will be the analysis of climatological data (monthly global surface air temperature) defining a climate complex network.
[1] O. A. Rosso and C. Masoller, Phys. Rev. E 79, 040106(R) (2009).
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