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Protocol-based control for semi-Markov reaction-diffusion neural networks.

Na Liu1, Wenjie Qin2, Jun Cheng3

  • 1Department of Mathematics, Yunnan Minzu University, Kunming, Yunnan, 650500, China; School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 28, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances control for semi-Markov reaction-diffusion neural networks (SMRDNNs) using a novel probabilistic event-triggered protocol (PETP). This improves system responsiveness and reliability in networked environments.

Keywords:
Probabilistic event-triggered protocolReaction–diffusion neural networksSemi-Markov jump systems

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Area of Science:

  • Control Theory
  • Artificial Neural Networks
  • Stochastic Systems

Background:

  • Addressing asynchronous control challenges in complex networked systems.
  • Managing stochastic behavior and communication delays is crucial for system performance.
  • Existing methods struggle with dynamic variations between system and controller modes.

Purpose of the Study:

  • To develop a robust asynchronous control strategy for semi-Markov reaction-diffusion neural networks (SMRDNNs).
  • To introduce a novel probabilistic event-triggered protocol (PETP) for enhanced system responsiveness.
  • To accurately model dynamic asynchronous variations in networked environments.

Main Methods:

  • Incorporation of a semi-Markov process with deterministic switching for stochastic behavior analysis.
  • Proposal of a novel PETP utilizing probabilistic delay division for dynamic transmission frequency adjustment.
  • Development of a dynamic asynchronous model to capture mode variations.

Main Results:

  • Effective mitigation of arbitrary switching impacts through the semi-Markov process.
  • Enhanced system responsiveness and reliability via the dynamically adjusted PETP.
  • Accurate modeling of asynchronous variations, validated by simulation.

Conclusions:

  • The proposed PETP and dynamic asynchronous model significantly improve control performance for SMRDNNs.
  • The strategies offer enhanced reliability and responsiveness in networked communication systems.
  • The developed approach demonstrates superiority in handling asynchronous control problems.