Anti-synchronization of complex-valued memristor-based delayed neural networks

Area of Science:

• Complex-valued neural networks
• Nonlinear dynamics
• Control theory

Background:

• Memristor-based neural networks exhibit complex dynamics.
• Time delays introduce challenges in synchronization analysis.
• Anti-synchronization is crucial for secure communication and signal processing.

Purpose of the Study:

• To investigate and achieve exponential anti-synchronization in complex-valued memristor-based neural networks with time delays.
• To design novel external controllers for guaranteeing anti-synchronization.
• To provide more general and less conservative results compared to existing literature.

Main Methods:

• Construction of appropriate Lyapunov functions.
• Application of inequality techniques for controller design.
• Derivation of two distinct types of controllers.

Main Results:

• Exponential anti-synchronization of complex-valued memristor-based delayed neural networks is guaranteed.
• The proposed controllers and results are shown to be more general and less conservative.
• Theoretical results are easily verifiable using system parameters.

Conclusions:

• The designed controllers effectively achieve exponential anti-synchronization.
• The study offers a more generalized framework for analyzing delayed memristor neural networks.
• Numerical simulations confirm the practical effectiveness of the proposed methods.