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Event-Triggered Exponential Stabilization for State-Based Switched Inertial Complex-Valued Neural Networks With
IEEE Transactions on Cybernetics
|November 25, 2020
Summary
This study stabilizes complex-valued neural networks using event-triggered control, ensuring efficient data transmission and preventing Zeno behavior for reliable system performance.
Area of Science:
- Control Theory
- Computational Neuroscience
- Complex Systems
Background:
- Complex-valued neural networks (CVNNs) are crucial for modeling complex phenomena.
- Stabilization of switched inertial CVNNs with multiple delays presents significant challenges.
- Event-triggered control offers efficient communication and reduced computational load.
Purpose of the Study:
- To investigate the exponential stabilization of state-based switched inertial complex-valued neural networks with multiple delays.
- To design an effective event-triggered control strategy for these networks.
- To ensure the absence of the Zeno phenomenon.
Main Methods:
- Modeling of state-based switched inertial complex-valued neural networks with multiple delays.
- Transformation of complex-valued networks into real-valued networks by separating real and imaginary parts.
- Variable substitution to convert second-order inertial networks into first-order networks.
- Design of an event-triggered controller with a transmission sequence.
- Construction of Lyapunov functions and application of inequalities to derive stabilization conditions.
Main Results:
- Sufficient conditions for the exponential stabilization of the proposed neural networks were derived.
- The designed event-triggered controller effectively prevents the Zeno phenomenon.
- Simulation results validated the theoretical findings.
Conclusions:
- The proposed event-triggered control strategy ensures exponential stabilization for switched inertial complex-valued neural networks with multiple delays.
- The method is computationally efficient and avoids Zeno oscillations.
- This work contributes to the robust control of complex dynamical systems.
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