Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue DOI
Igor Franović, Sebastian Eydam, Deniz Eroglu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(12)

Published: Dec. 1, 2024

Regime switching, the process where complex systems undergo transitions between qualitatively different dynamical states due to changes in their conditions, is a widespread phenomenon, from climate and ocean circulation, ecosystems, power grids, brain. Capturing mechanisms that give rise isolated or sequential switching dynamics, as well developing generic robust methods for forecasting, detecting, controlling them essential maintaining optimal performance preventing dysfunctions even collapses systems. This Focus Issue provides new insights into regime covering recent advances theoretical analysis harnessing reduction approaches, data-driven detection non-feedback control strategies. Some of key challenges addressed include development techniques coupled stochastic adaptive systems, influence multiple timescale dynamics on chaotic structures cyclic patterns forced role saddles heteroclinic cycles pattern oscillators. The contributions further highlight deep learning applications predicting grid failures, use blinking networks enhance synchronization, creating strategies epidemic spreading, suppress epileptic seizures. These developments are intended catalyze dialog branches complexity.

Language: Английский

Fluctuation induced intermittent transitions between distinct rhythms in balanced excitatory–inhibitory spiking networks DOI
Xiyun Zhang, Bojun Wang, Hongjie Bi

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 196, P. 116321 - 116321

Published: March 28, 2025

Language: Английский

Citations

0
Distributed optimization consensus for multi-agent systems on matrix-weighted networks DOI
Suoxia Miao,

Ruiying Xiong,

Qing An

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(12)

Published: Dec. 1, 2024

In this paper, the distributed optimization consensus issues for both first-order continuous time (CT) and discrete-time (DT) multi-agent systems (MASs) on matrix-weighted networks are studied. order to make each agent achieve consensus, a new algorithm CT DT MASs is proposed. Using Lyapunov stability theory matrix theory, conditions obtained, respectively. Finally, correctness of our results verifiied by numerical examples.

Language: Английский

Citations

2
Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue DOI
Igor Franović, Sebastian Eydam, Deniz Eroglu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(12)

Published: Dec. 1, 2024

Regime switching, the process where complex systems undergo transitions between qualitatively different dynamical states due to changes in their conditions, is a widespread phenomenon, from climate and ocean circulation, ecosystems, power grids, brain. Capturing mechanisms that give rise isolated or sequential switching dynamics, as well developing generic robust methods for forecasting, detecting, controlling them essential maintaining optimal performance preventing dysfunctions even collapses systems. This Focus Issue provides new insights into regime covering recent advances theoretical analysis harnessing reduction approaches, data-driven detection non-feedback control strategies. Some of key challenges addressed include development techniques coupled stochastic adaptive systems, influence multiple timescale dynamics on chaotic structures cyclic patterns forced role saddles heteroclinic cycles pattern oscillators. The contributions further highlight deep learning applications predicting grid failures, use blinking networks enhance synchronization, creating strategies epidemic spreading, suppress epileptic seizures. These developments are intended catalyze dialog branches complexity.

Language: Английский

Citations

0