Jun Cheng

11.1k total citations · 5 hit papers
379 papers, 9.0k citations indexed

About

Jun Cheng is a scholar working on Control and Systems Engineering, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Jun Cheng has authored 379 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 295 papers in Control and Systems Engineering, 235 papers in Computer Networks and Communications and 56 papers in Electrical and Electronic Engineering. Recurrent topics in Jun Cheng's work include Stability and Control of Uncertain Systems (245 papers), Neural Networks Stability and Synchronization (188 papers) and Distributed Control Multi-Agent Systems (58 papers). Jun Cheng is often cited by papers focused on Stability and Control of Uncertain Systems (245 papers), Neural Networks Stability and Synchronization (188 papers) and Distributed Control Multi-Agent Systems (58 papers). Jun Cheng collaborates with scholars based in China, South Korea and Saudi Arabia. Jun Cheng's co-authors include Ju H. Park, Jinde Cao, Shouming Zhong, Wenhai Qi, Kaibo Shi, Huaicheng Yan, Hong Zhu, Zheng‐Guang Wu, Hamid Reza Karimi and Dan Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Scientific Reports.

In The Last Decade

Jun Cheng

334 papers receiving 8.9k citations

Hit Papers

Non-fragile memory filter... 2016 2026 2019 2022 2019 2016 2021 2023 2024 50 100 150 200

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jun Cheng 6.2k 5.5k 1.4k 1.3k 1.2k 379 9.0k
Jianwei Xia 6.5k 1.1× 4.9k 0.9× 1.0k 0.7× 1.0k 0.8× 1.0k 0.9× 373 8.6k
Zhanshan Wang 3.6k 0.6× 4.1k 0.7× 2.1k 1.5× 1.2k 1.0× 1.8k 1.5× 261 7.2k
Chen Peng 8.9k 1.4× 6.9k 1.2× 1.8k 1.3× 553 0.4× 987 0.8× 299 11.1k
Baoyong Zhang 5.6k 0.9× 4.2k 0.8× 825 0.6× 726 0.6× 921 0.8× 213 7.4k
PooGyeon Park 7.5k 1.2× 5.3k 0.9× 1.1k 0.8× 746 0.6× 976 0.8× 320 10.2k
Hongli Dong 6.8k 1.1× 5.4k 1.0× 1.2k 0.9× 537 0.4× 2.5k 2.1× 289 9.8k
Shumin Fei 4.3k 0.7× 3.6k 0.6× 751 0.5× 910 0.7× 869 0.7× 373 6.2k
Magdi S. Mahmoud 6.8k 1.1× 3.4k 0.6× 1.4k 1.0× 464 0.4× 983 0.8× 477 8.5k
Derui Ding 8.2k 1.3× 7.3k 1.3× 1.9k 1.4× 491 0.4× 2.6k 2.1× 237 11.8k
Renquan Lu 7.8k 1.3× 5.3k 1.0× 937 0.7× 705 0.6× 1.3k 1.1× 207 10.3k

Countries citing papers authored by Jun Cheng

Since Specialization
Citations

This map shows the geographic impact of Jun Cheng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Cheng more than expected).

Fields of papers citing papers by Jun Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Cheng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Cheng. The network helps show where Jun Cheng may publish in the future.

Co-authorship network of co-authors of Jun Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Cheng. A scholar is included among the top collaborators of Jun Cheng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jun Cheng. Jun Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Pang, Zhen, et al.. (2025). Scaling policy iteration based reinforcement learning for unknown discrete-time linear systems. Automatica. 176. 112227–112227.
3.
Qi, Wenhai, et al.. (2025). Dynamic self-triggered control for positive fuzzy Markov jump systems with switching transition rates. Journal of the Franklin Institute. 362(5). 107583–107583. 1 indexed citations
4.
Cheng, Jun, et al.. (2025). Distributed filtering for T-S fuzzy systems under cyber-attacks with time-varying saturation function. Communications in Nonlinear Science and Numerical Simulation. 143. 108624–108624.
5.
Li, Yulong, et al.. (2024). Static output feedback LFC for semi‐Markov type interconnected multi‐area power systems: A non‐fragile PI strategy. International Journal of Robust and Nonlinear Control. 34(15). 10437–10453. 2 indexed citations
6.
Cheng, Jun, et al.. (2024). Probability-based event-triggered asynchronous control for fuzzy jump systems. Information Sciences. 680. 121167–121167. 1 indexed citations
7.
Li, Xiaoqing, Kun She, Kaibo Shi, et al.. (2024). Nonfragile switched sampled-data control for ship electric propulsion systems with stochastic actuator failures: A dual-sided looped fuzzy Lyapunov functional. Fuzzy Sets and Systems. 483. 108914–108914. 7 indexed citations
8.
Zhang, Lihua, et al.. (2024). Dynamic self-triggered protocol for Takagi-Sugeno fuzzy positive Markov switching systems. Information Sciences. 695. 121733–121733. 1 indexed citations
9.
Qi, Wenhai, et al.. (2024). Sliding mode control for discrete interval type-2 fuzzy semi-Markov jump models with delay in controller mode switching. Fuzzy Sets and Systems. 483. 108915–108915. 4 indexed citations
10.
Qi, Wenhai, et al.. (2024). SMC for discrete singular stochastic jump systems under semi-Markov kernel. Journal of the Franklin Institute. 361(17). 107250–107250. 2 indexed citations
11.
Cheng, Jun, et al.. (2024). Protocol-based SMC for singularly perturbed systems with switching parameters and deception attacks. Information Sciences. 679. 121089–121089.
12.
Wang, Jun, et al.. (2024). Enhanced cubic function negative-determination Lemma on stability analysis for delayed neural networks via new analytical techniques. Journal of the Franklin Institute. 361(3). 1155–1166. 3 indexed citations
13.
Ye, Zehua, et al.. (2024). Distributed finite-time secure filtering for T-S fuzzy systems under hybrid cyber-attacks: Application to tunnel diode circuits. Applied Mathematics and Computation. 480. 128900–128900. 1 indexed citations
14.
Cheng, Jun, et al.. (2024). Event-based asynchronous state estimation for Markov jump memristive neural networks. Applied Mathematics and Computation. 473. 128653–128653. 5 indexed citations
16.
Chen, Yongyi, Dan Zhang, Hongjie Ni, Jun Cheng, & Hamid Reza Karimi. (2023). Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery. Engineering Applications of Artificial Intelligence. 123. 106181–106181. 22 indexed citations
17.
Cheng, Jun, Jiangming Xu, Ju H. Park, Huaicheng Yan, & Dan Zhang. (2023). Protocol-based SMC for singularly perturbed switching systems with sojourn probabilities. Automatica. 161. 111470–111470. 43 indexed citations
18.
Liu, Xingyue, Kaibo Shi, Jun Cheng, Shiping Wen, & Yajuan Liu. (2023). Adaptive memory-based event-triggering resilient LFC for power system under DoS attack. Applied Mathematics and Computation. 451. 128041–128041. 13 indexed citations
19.
Cheng, Jun, et al.. (2023). Finite-time optimal control for Markov jump systems with singular perturbation and hard constraints. Information Sciences. 632. 454–466. 10 indexed citations
20.
Cheng, Jun, et al.. (2023). Sliding Mode Control for Fuzzy Singularly Perturbed Systems With Improved Protocol. IEEE Transactions on Circuits & Systems II Express Briefs. 70(8). 3039–3043. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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