Quasi-Synchronization of Fractional Multiweighted Coupled Neural Networks via Aperiodic Intermittent Control

Area of Science:

• Control Theory
• Nonlinear Dynamics
• Computational Neuroscience

Background:

• Fractional-order systems offer complex dynamics.
• Coupled neural networks are crucial for modeling complex behaviors.
• Discontinuous activation functions and parameter mismatches present significant challenges in synchronization analysis.

Purpose of the Study:

• To investigate quasi-synchronization in fractional multiweighted coupled neural networks (FMCNNs).
• To address challenges posed by discontinuous activation functions and mismatched parameters.
• To develop novel theoretical tools for analyzing fractional-order systems.

Main Methods:

• Development of a novel piecewise fractional differential inequality based on the generalized Caputo fractional-order derivative.
• Application of Lyapunov stability theory.
• Design of aperiodic intermittent control strategies.

Main Results:

• Establishment of a novel fractional differential inequality extending existing results.
• Derivation of sufficient conditions for quasi-synchronization of FMCNNs.
• Explicit determination of the exponential convergence rate and synchronization error bound.

Conclusions:

• The proposed methods effectively achieve quasi-synchronization for FMCNNs.
• The developed fractional inequality provides a powerful tool for analyzing fractional-order systems.
• Numerical simulations validate the theoretical findings and the efficacy of the control strategy.