Dongsheng Xu

Orcid: 0000-0002-6542-6358

Affiliations:
  • Harbin Institute of Technology at Weihai, Weihai, China


According to our database1, Dongsheng Xu authored at least 12 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2025
Volatile Discrete-Time Observation Stabilization of Time-Varying Hybrid Stochastic Large-Scale Networks.
IEEE Trans Autom. Sci. Eng., 2025

Quasi-Synchronization of Heterogeneous Hybrid Stochastic Delayed Networks via Pinning Intermittent Discrete Observation Control.
IEEE Trans Autom. Sci. Eng., 2025

An Intermittent Volatile Event-Triggered Synchronization Approach for Multi-Layer Networks With Noise Coupling Under an Almost Sure Framework.
IEEE Trans Autom. Sci. Eng., 2025

2024
Dynamic periodic event-triggered control for input-to-state stability of multilayer coupled systems.
Int. J. Control, March, 2024

Quasi-synchronization of stochastic heterogeneous networks via intermittent pinning sampled-data control.
Expert Syst. Appl., March, 2024

Aperiodically Intermittent Pinning Event-Triggered Synchronization of Stochastic Heterogeneous Complex Networks.
IEEE Trans. Netw. Sci. Eng., 2024

2023
Alternate Event-Triggered Intermittent Control for Exponential Synchronization of Multi-Weighted Complex Networks.
Neural Process. Lett., June, 2023

Delay Event-Triggered Control for Stability Analysis of Complex Networks.
IEEE Trans. Circuits Syst. II Express Briefs, March, 2023

Exponential synchronization of multilayer networks with white-noise-based coupling via intermittent periodic event-triggered control.
J. Frankl. Inst., 2023

2022
Alternate periodic event-triggered control for synchronization of multilayer neural networks.
Inf. Sci., 2022

Aperiodically intermittent pinning discrete-time observation control for exponential synchronization of stochastic multilayer coupled systems.
Neurocomputing, 2022

2021
Bipartite synchronization of signed networks via aperiodically intermittent control based on discrete-time state observations.
Neural Networks, 2021


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