Jun‐Juh Yan

3.9k citations
166 papers · 3.2k · h-index 33

Impact in

Papers in

Jun‐Juh Yan

156 papers receiving 3.1k citations

Peers

Jun‐Juh Yan
Comparison fields: 5 of 85
  • Statistical and Nonlinear Physics 1.8k
  • Computer Networks and Communications 1.8k
  • Control and Systems Engineering 1.0k
  • Computer Vision and Pattern Recognition 350
  • Modeling and Simulation 60
Replace Teh‐Lu Liao with:
Teh‐Lu Liao Taiwan
Binoy Krishna Roy India
Zhuzhi Yuan China
Chongxin Liu China
Guoyuan Qi China
Sangchul Won South Korea
Abdesselem Boulkroune Algeria
Mohammad Pourmahmood Aghababa Iran
Eyad H. Abed United States
Emmanuel Moulay France
Jun‐Juh Yan relative to Teh‐Lu Liao Taiwan Teh‐Lu Liao's profile →
Citations per field
00.5×1.5×
Teh‐Lu Liao · 1×
Citations per year

Countries citing papers authored by Jun‐Juh Yan

Since Specialization
Citations

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

Fields of papers citing papers by Jun‐Juh Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun‐Juh Yan, 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‐Juh Yan Line = papers co-authored together Jun‐Juh Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 166 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1993150
2 2005127
3 200693
4 200690
5 200689
6 200684
7 200681
8 200678
9 200772
10 200367
11 200666
12 200865
13 201654
14 200952
15 200551
16 200450
17 200749
18 200748
19 200347
20 200745

About Jun‐Juh Yan

Jun‐Juh Yan is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Control and Systems Engineering, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 166 papers that have together received 3.2k indexed citations. Recurring topics across this work include Chaos control and synchronization (89 papers), Nonlinear Dynamics and Pattern Formation (63 papers), Neural Networks Stability and Synchronization (55 papers), Quantum chaos and dynamical systems (40 papers), Chaos-based Image/Signal Encryption (25 papers), Adaptive Control of Nonlinear Systems (23 papers), Stability and Control of Uncertain Systems (20 papers) and Cellular Automata and Applications (12 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.8k citations), Computer Networks and Communications (1.8k citations), Control and Systems Engineering (1.0k citations), Computer Vision and Pattern Recognition (350 citations) and Modeling and Simulation (60 citations). Jun‐Juh Yan has collaborated with scholars based in Taiwan, China and United States. Frequent co-authors include Teh‐Lu Liao, Her‐Terng Yau, Meei-Ling Hung, Kuo‐Kai Shyu, Yi‐You Hou, Wei‐Der Chang, Chi‐Chuan Hwang, Jason Sheng-Hong Tsai, Chao‐Lin Kuo and F. Thiele. Their work appears in journals such as Chaos Solitons & Fractals, International Journal of Nonlinear Sciences and Numerical Simulation, Expert Systems with Applications, Physics Letters A and Journal of Dynamic Systems Measurement and Control.

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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