Synchronization in Fractional-Order Complex-Valued Delayed Neural Networks

Area of Science:

• Complex Systems and Networks
• Nonlinear Dynamics
• Control Theory

Background:

• Fractional order complex valued neural networks (FOCVNN) are advanced models with applications in various fields.
• Time delays are common in real-world systems and can significantly impact network dynamics.
• Synchronization of neural networks is crucial for information processing and secure communication.

Purpose of the Study:

• To investigate and achieve synchronization for FOCVNN in the presence of time delays.
• To develop robust synchronization criteria for delayed fractional order systems.
• To validate the proposed synchronization approach through numerical simulations.

Main Methods:

• Utilizing a linear feedback control strategy for synchronization.
• Applying the comparison theorem for fractional order linear systems with delay.
• Conducting numerical simulations to demonstrate feasibility and effectiveness.

Main Results:

• Successfully derived synchronization criteria for FOCVNN with time delays.
• Demonstrated the effectiveness of the proposed linear feedback control.
• Numerical simulations confirmed the theoretical findings.

Conclusions:

• The proposed method effectively synchronizes fractional order complex valued neural networks with time delays.
• The employed control strategy and comparison theorem provide a reliable framework for synchronization.
• This research contributes to the understanding and control of complex dynamical systems.