Special Issue "Recent Advances in Chaos Theory and Complex Networks"
Deadline for manuscript submissions: closed (30 April 2015)
Prof. Dr. Mustak E. Yalcin
Department of Electronics and Communication Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
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Interests: nonlinear circuits and systems; neural networks; complex systems; nonlinear dynamics; complex networks
As one of the greatest natural science discoveries in the 20th century, chaos theory has been extensively investigated, and significant progress has been made in the past several decades. It has become an exciting emerging interdisciplinary area in which a broad spectrum of methodologies and technologies are being studied to deal with large, complex, and dynamical problems. In the last fifteen years, the notion of complex networks as part of the complexity studies is actively and intensively investigated in the broad areas of computer and communication networks, physical and biological networks, as well as social and financial networks. In particular, chaotic networks, i.e., networks of chaotic maps, have emerged as a new trend of proactive research, extending the classical subject of coupled map lattices regarding their dynamical properties such as spatiotemporal chaos, bifurcations and entropy.
The main focus of this Special Issue will be on the state-of-the-art advancements in chaos theory and complex networks, as well as their applications to dynamical systems, nonlinear circuits, information processing, communications, cryptography, systems biology, and so on.
Prof. Dr. Guanrong Chen
Prof. Dr. C.K. Michael Tse
Prof. Dr. Mustak E. Yalcin
Dr. Hai Yu
Dr. Mattia Frasca
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- complex network
- fractional calculus
- information theory
- nonlinear dynamics
- systems biology
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Dynamical Systems from Networks Constructed from Time Series"
Authors: Lvlin Hou 1 and Michael Small 2
Affiliations: 1 National University of Defence Technology, China
2 The University of Western Australia
Abstract: Several methods exist to construct complex networks from time series. In general these methods claim to construct complex networks which preserve certain properties of the underlying time series. In this paper we test this assertion by developing an algorithm to realise dynamical systems from these complex networks in such a way that trajectories of these dynamical systems are time series that preserve certain statistical properties of the original time series (and hence the underlying true dynamical system). Using this algorithm we are able to demonstrate that the so-called $k$-nearet neighbour adaptive algorithm for generating networks out-performs methods based of $\epsilon$-ball recurrence plots. For such networks - and with suitable choice of parameter values, which we provide - the time series generated by this method function as a nonlinear surrogate generation algorithm. Hence, one can use this approach to test the null hypothesis of a noise driven nonlinear periodic orbit in observed time series data - with the alternative hypothesis of deterministic chaos.
Author: Marc Brachet
Affiliation: Centre National de la Recherche Scientifique, Laboratoire de Physique Statistique, Université Pierre-et-Marie-Curie Paris 06, Université Paris Diderot, Ecole Normale Supérieure, 75231 Paris Cedex 05, France