This paper addresses the asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs) under probabilistic event-triggered protocol (PETP) scheduling. A semi-Markov process with a deterministic switching rule is introduced to characterize the stochastic behavior of these networks, effectively mitigating the impacts of arbitrary switching. Leveraging statistical data on communication-induced delays, a novel PETP is proposed that adjusts transmission frequencies through a probabilistic delay division method. The dynamic adjustment of event trigger conditions based on real-time neural network is realized, and the responsiveness of the system is enhanced, which is of great significance for improving the performance and reliability of the communication system. Additionally, a dynamic asynchronous model is introduced that more accurately captures the variations between system modes and controller modes in the network environment. Ultimately, the efficacy and superiority of the developed strategies are validated through a simulation example.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neunet.2024.106556 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion terms.
Neural Netw
December 2024
School of Engineering, Qufu Normal University, Rizhao 273165, China.
This paper investigates the probabilistic-sampling-based asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs). Aiming at mitigating the drawback of the well-known fixed-sampling control law, a more general probabilistic-sampling-based control strategy is developed to characterize the randomly sampling period. The system mode is considered to be related to the sojourn-time and undetectable.
View Article and Find Full Text PDFProtocol-based control for semi-Markov reaction-diffusion neural networks.
Neural Netw
November 2024
Research Center of Automation and Artificial Intelligence, Zhejiang University of Technology, Hangzhou 310014, China.
This paper addresses the asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs) under probabilistic event-triggered protocol (PETP) scheduling. A semi-Markov process with a deterministic switching rule is introduced to characterize the stochastic behavior of these networks, effectively mitigating the impacts of arbitrary switching. Leveraging statistical data on communication-induced delays, a novel PETP is proposed that adjusts transmission frequencies through a probabilistic delay division method.
View Article and Find Full Text PDFFinite-time synchronization of delayed semi-Markov reaction-diffusion systems: An asynchronous boundary control scheme.
ISA Trans
May 2024
Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-performance Mechanical Equipment, Changsha University of Science and Technology, Changsha 410114, China; Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China. Electronic address:
This paper tries to study the problem of finite-time synchronization for delayed semi-Markov reaction-diffusion systems. Based on the spatial and parametric characteristics of the considered systems, a new asynchronous boundary control scheme is proposed to ensure the finite-time synchronization of the drive and response systems. In the asynchronous boundary control scheme, only an actuator should be placed at the spatial boundary, which is more easier to implement and economical than the other non-boundary control strategies.
View Article and Find Full Text PDFExponential Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs via Event-Based Spatially Pointwise-Piecewise Switching Control.
IEEE Trans Neural Netw Learn Syst
February 2024
This article considers both the semi-Markov jumping phenomenon and spatial distribution characteristics when investigating the exponential stabilization of memristive neural networks (MNNs). The introduction of the semi-Markov jumping parameters relaxes the restriction on the sojourn time of Markovian MNNs. To increase the operability while ensuring control effect, a novel event-based spatially pointwise-piecewise switching control scheme is presented under a unified spatial division criterion, in which the pointwise and piecewise control can switch according to the preset event condition for the applicability to different control requirements.
View Article and Find Full Text PDFRenewal Reward Perspective on Linear Switching Diffusion Systems in Models of Intracellular Transport.
Bull Math Biol
September 2020
Department of Mathematics, Tulane University, New Orleans, USA.
In many biological systems, the movement of individual agents is characterized having multiple qualitatively distinct behaviors that arise from a variety of biophysical states. For example, in cells the movement of vesicles, organelles, and other intracellular cargo is affected by their binding to and unbinding from cytoskeletal filaments such as microtubules through molecular motor proteins. A typical goal of theoretical or numerical analysis of models of such systems is to investigate effective transport properties and their dependence on model parameters.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!