Relaxed Exponential Stabilization for Coupled Memristive Neural Networks With Connection Fault and Multiple Delays...

Area of Science:

• Control Theory
• Neural Networks
• Network Security

Background:

• Coupled memristive neural networks (CMNNs) are susceptible to connection faults and multiple delays, potentially causing cascading failures.
• Existing event-triggered mechanisms may lack robustness against noise and faults in complex network systems.
• Fault tolerance and survivability are critical for reliable operation of CMNNs.

Purpose of the Study:

• To investigate relaxed exponential stabilization for CMNNs with connection faults and multiple delays.
• To develop an optimized elastic event-triggered mechanism (OEEM) for enhanced robustness and fault tolerance.
• To ensure the globally uniformly ultimately bounded (GUUB) stability of the CMNNs under adverse conditions.

Main Methods:

• Design of an optimized elastic event-triggered mechanism (OEEM) incorporating time-varying bounded noise threshold matrices, decreased exponential threshold functions, and adaptive functions.
• Construction of Lyapunov-Krasovskii functionals (LKFs) with improved delay-product-type terms.
• Application of inequality processing techniques to derive stabilization and boundedness conditions.

Main Results:

• The proposed OEEM enhances the robustness of event-triggered control for CMNNs, effectively handling noise signals.
• Relaxed exponential stabilization and GUUB conditions are derived for CMNNs with connection faults and multiple delays.
• The method demonstrates improved fault-tolerant capability and survivability through the integration of backup resources.

Conclusions:

• The developed OEEM provides an effective strategy for achieving stable and robust control of CMNNs.
• The study confirms the feasibility of the proposed approach through numerical examples, highlighting its practical applicability.
• This research contributes to the advancement of secure and reliable memristive neural network systems.