Abstract

Scaling laws for the diffusion generated by three different random walk models are reviewed. The random walks, defined on a one-dimensional lattice, are driven by renewal intermittent events with non-Poisson statistics and inverse power-law tail in the distribution of the inter-event or waiting times, so that the event sequences are characterized by self-similarity. Intermittency is a ubiquitous phenomenon in many complex systems and the power exponent of the waiting time distribution, denoted as complexity index, is a crucial parameter characterizing the system's complexity. It is shown that different scaling exponents emerge from the different random walks, even if the self-similarity, i.e. the complexity index, of the underlying event sequence remains the same. The direct evaluation of the complexity index from the time distribution is affected by the presence of added noise and secondary or spurious events. It is possible to minimize the effect of spurious events by exploiting the scaling relationships of the random walk models. This allows to get a reliable estimation of the complexity index and, at the same time, a confirmation of the renewal assumption. An application to turbulence data is shown to explain the basic ideas of this approach.

Autore Pugliese

• Donateo Antonio , Contini Daniele , Cesari Rita

Tutti gli autori

• Paradisi P.; Cesari R.; Donateo A.; Contini D.; Allegrini P.

Titolo volume/Rivista

Reports on mathematical physics

Anno di pubblicazione

2012

ISSN

0034-4877

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile