Exponential Synchronization of Markovian Jump Neural Networks Based on Asynchronous Delayed-Feedback Controller With...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Complex network environments often cause delays in feedback information, hindering controller performance.
• Achieving timely synchronization in Markovian jump neural networks is challenging due to these environmental complexities.

Purpose of the Study:

• To propose a novel asynchronous delayed-feedback controller for achieving exponential synchronization in Markovian jump neural networks.
• To address the issue of delayed feedback information in complex network environments.

Main Methods:

• Designing a new asynchronous delayed-feedback controller that accounts for feedback delay.
• Utilizing a novel Lyapunov functional to derive quantized relationships between exponential synchronization and feedback delay, establishing delay boundaries.
• Employing a hidden-Markov process to model controller asynchrony, allowing flexible controller modes.

Main Results:

• Established delay boundaries for exponential synchronization in the presence of feedback delay.
• Demonstrated the controller's applicability in both synchronous and asynchronous scenarios.
• Significantly augmented the computational freedom of the controller gain matrix.

Conclusions:

• The proposed method effectively achieves exponential synchronization for Markovian jump neural networks under asynchronous and delayed feedback conditions.
• The approach offers a breakthrough by assuming bounded known detection probability and enhances computational flexibility.
• Numerical simulations confirm the method's superiority and effectiveness over existing techniques.