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Summary

This summary is machine-generated.

This study explores quasi-projective synchronization (Q-PS) in uncertain discrete-time fractional-order delayed memristive neural networks (DFDMNNs). New criteria ensure solutions exist and achieve Q-PS using feedback control.

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Area of Science:

Computational Neuroscience
Chaos Theory
Nonlinear Dynamics

Background:

Memristive neural networks (MNNs) exhibit complex dynamics.
Fractional-order calculus offers more realistic system modeling.
Discrete-time systems and time delays are crucial in real-world networks.

Purpose of the Study:

To investigate quasi-projective synchronization (Q-PS) in discrete-time fractional-order delayed memristive neural networks (DFDMNNs) with uncertainties.
To establish sufficient conditions for the existence of solutions in these complex networks.
To develop criteria for achieving Q-PS using delayed state feedback control.

Main Methods:

Utilizing inequality techniques and properties of discrete-time fractional calculus.
Applying fixed-point theorems to guarantee the existence of solutions.
Designing a delayed state feedback controller to achieve Q-PS.

Main Results:

Derived several sufficient criteria for the existence of solutions for DFDMNNs with uncertainties.
Established corresponding criteria for achieving Q-PS in DFDMNNs.
Demonstrated the effectiveness of the theoretical results through a numerical example.

Conclusions:

The proposed methods effectively guarantee the existence of solutions for uncertain DFDMNNs.
The developed criteria ensure successful quasi-projective synchronization in these systems.
Numerical simulations validate the theoretical findings and the controller's performance.