Experimental datasets of networks of nonlinear oscillators: Structure and dynamics during the path to synchronization

Vera-Ávila, V. P.; Sevilla-Escoboza, R.; Lozano-Sánchez, A.A.; Rivera-Durón, Roberto R.; Buldú, Javier M.

doi:10.1016/j.dib.2019.105012

Cited by 14 publications

(18 citation statements)

References 4 publications

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“…For each ρ analyzed value, the initial conditions are changed through a relay circuit to explore the whole system dynamics, where the time series from the three state variables are stored in a computer through a data acquisition board (DAQ). The DAQ board used for this purpose is an NI USB 6353 [2] . Unlike the numerical, where there is independent control over the three initial conditions of the state variables, in the electronic implementation, the three initial conditions are randomly modified at the same time.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Electronic implementation dataset to monoparametric control the number of scrolls generated

et al. 2020

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The increasing usage of chaotic systems in the development of security systems, from mobile surveillance devices to the implementation of secure communication systems, leads to devising analog electronic implementations of research. This article presents the electronic implementation dataset from a multi-scroll chaotic system capable of generates until 9-scrolls throughout a monoparametric control based on a Saturated Non-Lineal Function (SNLF). The implemented system controls the number of generated scrolls being capable of producing attractors of single scroll and attractors with 3, 5, 7, and 9-scrolls. Moreover, the implemented system produces a family of bistable behaviors. The authors report the phenomenon in [1] , where a complete analysis in the system has been carried out.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Electronic implementation dataset to monoparametric control the number of scrolls generated

et al. 2020

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“…Equations describing the dynamics of the electronic Rössler oscillators are 24 , 29 , 30 : where , and are the three voltages that describe the dynamical state of oscillator i , R and C are resistances and capacitors, is the coupling strength (controlled by a digital potentiometer) and are the elements of the adjacency matrix A , with if the output of oscillator i is used as the input of oscillator j and zero if both circuits are not connected in the direction . Finally, is a piecewise nonlinear function given by: …”

Section: Methodsmentioning

confidence: 99%

“…We use the Rössler system described in Ref. 30 , which is composed of a combination of resistances, capacitors, diodes and operational amplifiers (Op-Amp). All parameters are fixed and equal to all oscillators.…”

Section: Methodsmentioning

confidence: 99%

Identifiability of structural networks of nonlinear electronic oscillators

Vera-Ávila

Sevilla-Escoboza

Goñi

et al. 2020

Sci Rep

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Parameters of the electronic components. We set the parameters of Eqs. (2)-(4) in order to have chaotic dynamics at the Rössler oscillators. The main advantage of being in such a regime is that the order parameter r is a good indicator of the global synchronization of the network, which allows relating the reported values of the identifiability parameter with the level of synchronization of the whole network. The specific values of the electronic components are summarized in Table 1. Circuit diagrams. The Rössler oscilator. We use the Rössler system described in Ref. 30 , which is composed of a combination of resistances, capacitors, diodes and operational amplifiers (Op-Amp). All parameters are fixed and equal to all oscillators. Figure 7 contains the circuit diagram.

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“…39 and for Ref. 41 ). However, as we will discuss later, the temporal filtering performed by the experimental data acquisition system prevents obtaining a good network reconstruction.…”

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confidence: 92%

Inferring the connectivity of coupled chaotic oscillators using Kalman filtering

Forero-Ortiz

Tirabassi

Masoller

et al. 2021

Sci Rep

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Inferring the interactions between coupled oscillators is a significant open problem in complexity science, with multiple interdisciplinary applications. While the Kalman filter (KF) technique is a well-known tool, widely used for data assimilation and parameter estimation, to the best of our knowledge, it has not yet been used for inferring the connectivity of coupled chaotic oscillators. Here we demonstrate that KF allows reconstructing the interaction topology and the coupling strength of a network of mutually coupled Rössler-like chaotic oscillators. We show that the connectivity can be inferred by considering only the observed dynamics of a single variable of the three that define the phase space of each oscillator. We also show that both the coupling strength and the network architecture can be inferred even when the oscillators are close to synchronization. Simulation results are provided to show the effectiveness and applicability of the proposed method.

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Experimental datasets of networks of nonlinear oscillators: Structure and dynamics during the path to synchronization

Cited by 14 publications

References 4 publications

Electronic implementation dataset to monoparametric control the number of scrolls generated

Electronic implementation dataset to monoparametric control the number of scrolls generated

Identifiability of structural networks of nonlinear electronic oscillators

Inferring the connectivity of coupled chaotic oscillators using Kalman filtering

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