Jun‐Guo Lu

3.9k citations
172 papers · 2.4k · h-index 25

Impact in

Papers in

Jun‐Guo Lu

155 papers receiving 2.4k citations

Peers

Jun‐Guo Lu
Comparison fields: 5 of 104
  • Modeling and Simulation 758
  • Control and Systems Engineering 1.3k
  • Statistical and Nonlinear Physics 630
  • Computer Networks and Communications 712
  • Numerical Analysis 147
Replace Naser Pariz with:
Naser Pariz Iran
Mohammad Pourmahmood Aghababa Iran
Jun Shen China
S. Marshal Anthoni India
Isabel S. Jesus Portugal
Frédéric Mazenc France
Sara Dadras United States
Reza Ghaderi Iran
Meng Zhang China
M. Syed Ali India
Jun‐Guo Lu relative to Naser Pariz Iran Naser Pariz's profile →
Citations per field
00.5×3.4×
Naser Pariz · 1×
Citations per year

Countries citing papers authored by Jun‐Guo Lu

Since Specialization
Citations

This map shows the geographic impact of Jun‐Guo Lu'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‐Guo Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun‐Guo Lu more than expected).

Fields of papers citing papers by Jun‐Guo Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun‐Guo Lu. 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‐Guo Lu. The network helps show where Jun‐Guo Lu may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun‐Guo Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun‐Guo Lu Line = papers co-authored together Jun‐Guo Lu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 172 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009246
2 2008175
3 201977
4 202071
5 202169
6 202067
7 201259
8 201257
9 200555
10 202054
11 201652
12 202248
13 202448
14 200645
15 201439
16 202038
17 201338
18 202435
19 200335
20 202135

About Jun‐Guo Lu

Jun‐Guo Lu is a scholar working on Control and Systems Engineering, Modeling and Simulation, Computer Networks and Communications, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 172 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advanced Control Systems Design (63 papers), Fractional Differential Equations Solutions (45 papers), Stability and Control of Uncertain Systems (44 papers), Adaptive Control of Nonlinear Systems (26 papers), Neural Networks Stability and Synchronization (23 papers), Chaos control and synchronization (18 papers), Nonlinear Dynamics and Pattern Formation (17 papers) and Iterative Learning Control Systems (16 papers). The work is most often cited by research in Modeling and Simulation (758 citations), Control and Systems Engineering (1.3k citations), Statistical and Nonlinear Physics (630 citations), Computer Networks and Communications (712 citations) and Numerical Analysis (147 citations). Jun‐Guo Lu has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Feifei Du, Guanrong Chen, Qing‐Hao Zhang, YangQuan Chen, Zheng-Mao Wu, Zhen Zhu, Weidong Chen, Zhaowu Ping, Chuang Li and Yugeng Xi. Their work appears in journals such as IEEE Transactions on Circuits & Systems II Express Briefs, International Journal of Robust and Nonlinear Control, Asian Journal of Control, ISA Transactions and IEEE Transactions on Systems Man and Cybernetics Systems.

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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