Related Experiment Video
Updated: Nov 22, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
Finite-time cluster synchronization in complex-variable networks with fractional-order and nonlinear coupling
Shuai Yang1, Cheng Hu1, Juan Yu1
1College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, Xinjiang, PR China.
This study achieves finite-time cluster synchronization for fractional-order complex networks using a non-decomposition method. The research introduces novel control strategies and criteria for synchronization, estimating the setting time effectively.
Area of Science:
- Complex Networks
- Nonlinear Dynamics
- Control Theory
Background:
- Fractional-order systems exhibit complex behaviors.
- Cluster synchronization is crucial for network dynamics.
- Nonlinear coupling presents synchronization challenges.
Purpose of the Study:
- To investigate finite-time cluster synchronization in fractional-order complex-variable networks.
- To develop novel control strategies for achieving synchronization.
- To establish theoretical criteria and estimate synchronization time.
Main Methods:
- Non-decomposition method applied to fractional-order systems.
- Design of complex-valued sign function-based controllers.
- Fractional-order stability theory and complex function theory utilized.
- A new norm incorporating real and imaginary components established.
- Fractional-order Caputo derivative and Mittag-Leffler functions applied for time estimation.
Main Results:
- Criteria for finite-time cluster synchronization derived.
- Effective estimation of the finite-time setting time achieved.
- Validation of theoretical results through two numerical examples.
Conclusions:
- The proposed non-decomposition method effectively achieves finite-time cluster synchronization.
- The developed control strategies and criteria are robust for fractional-order complex networks.
- The study provides a valuable framework for analyzing and controlling complex dynamical systems.
More Related Videos
07:59Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
Published on: June 9, 2023
05:59Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
Published on: October 6, 2023
Related Concept Videos
Linear time-invariant Systems
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
Transmission-Line Differential Equations
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...