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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Related Experiment Video

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Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion

Wanying Wei1, Dian Zhang2, Jun Cheng1

  • 1School of Mathematics and Statistics, Center for Applied Mathematics of Guangxi, Guangxi Normal University, Guilin, 541006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic-sampling control for semi-Markov reaction-diffusion neural networks (SMRDNNs), improving upon fixed-sampling methods. The research ensures network stability under asynchronous and random sampling conditions.

Keywords:
Randomly sampling intervalReaction–diffusion termsSampled-data controlSemi-Markov process

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Area of Science:

  • Control Theory
  • Neural Networks
  • Stochastic Systems

Background:

  • Semi-Markov reaction-diffusion neural networks (SMRDNNs) present complex control challenges.
  • Existing fixed-sampling control laws have limitations in handling random sampling periods.
  • Asynchronous system and controller mode jumping requires advanced control strategies.

Purpose of the Study:

  • To develop a probabilistic-sampling-based asynchronous control strategy for SMRDNNs.
  • To address the limitations of fixed-sampling control in SMRDNNs.
  • To ensure the asymptotic stability of SMRDNNs under random sampling and asynchronous conditions.

Main Methods:

  • Utilized a hidden semi-Markov model to characterize system dynamics.
  • Employed a stochastic analysis approach for stability investigation.
  • Developed a probabilistic-sampling control law to manage random sampling intervals.

Main Results:

  • Established sufficient conditions for the asymptotic stability of SMRDNNs.
  • Demonstrated the effectiveness of the probabilistic-sampling control strategy.
  • Showcased improved performance compared to fixed-sampling methods.

Conclusions:

  • The proposed probabilistic-sampling control is effective for SMRDNNs.
  • The method ensures asymptotic stability despite asynchronous and random sampling.
  • The findings offer a more general and superior approach to SMRDNN control.