Ning Tan
Impact in
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- Robotic Mechanisms and Dynamics
- Robot Manipulation and Learning
- Adaptive Control of Nonlinear Systems
- Iterative Learning Control Systems
- Biomedical Engineering top 5%
- Soft Robotics and Applications
Papers in
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- Robotic Mechanisms and Dynamics 36
- Robot Manipulation and Learning 24
- Iterative Learning Control Systems 19
- Adaptive Control of Nonlinear Systems 15
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- Soft Robotics and Applications 29
- Analog and Mixed-Signal Circuit Design 10
- Co-authors
- Peng Yu (35 shared papers)Yunong Zhang (24 shared papers)Deyue Yan (6 shared papers)Guyu Xiao (6 shared papers)Xiaoyi Gu (6 shared papers)Hongliang Ren (5 shared papers)Fenglei Ni (11 shared papers)Binghuang Cai (2 shared papers)
In The Last Decade
Ning Tan
135 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 111
- Control and Systems Engineering 705
- Biomedical Engineering 632
- Computer Vision and Pattern Recognition 253
- Computational Mathematics 7
- Mechanical Engineering 378
Countries citing papers authored by Ning Tan
This map shows the geographic impact of Ning Tan'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 Ning Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ning Tan more than expected).
Fields of papers citing papers by Ning Tan
This network shows the impact of papers produced by Ning Tan. 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 Ning Tan. The network helps show where Ning Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ning Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 150 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 111 | |
| 2 | 2009 | 66 | |
| 3 | 2020 | 61 | |
| 4 | 2018 | 57 | |
| 5 | 2016 | 44 | |
| 6 | 2016 | 43 | |
| 7 | 2017 | 42 | |
| 8 | 2021 | 41 | |
| 9 | 2010 | 40 | |
| 10 | 2022 | 37 | |
| 11 | 2010 | 37 | |
| 12 | 2020 | 36 | |
| 13 | 2020 | 35 | |
| 14 | 2021 | 29 | |
| 15 | 2010 | 29 | |
| 16 | 2022 | 28 | |
| 17 | 1994 | 27 | |
| 18 | 2017 | 27 | |
| 19 | 2015 | 26 | |
| 20 | 2000 | 26 |
About Ning Tan
Ning Tan is a scholar working on Control and Systems Engineering, Biomedical Engineering, Mechanical Engineering, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 150 papers that have together received 1.7k indexed citations. Recurring topics across this work include Robotic Mechanisms and Dynamics (36 papers), Soft Robotics and Applications (29 papers), Robot Manipulation and Learning (24 papers), Iterative Learning Control Systems (19 papers), Adaptive Control of Nonlinear Systems (15 papers), Modular Robots and Swarm Intelligence (11 papers), Robotic Path Planning Algorithms (11 papers) and Analog and Mixed-Signal Circuit Design (10 papers). The work is most often cited by research in Control and Systems Engineering (705 citations), Biomedical Engineering (632 citations), Computer Vision and Pattern Recognition (253 citations), Computational Mathematics (7 citations) and Mechanical Engineering (378 citations). Ning Tan has collaborated with scholars based in China, Singapore and Sweden. Frequent co-authors include Peng Yu, Yunong Zhang, Deyue Yan, Guyu Xiao, Xiaoyi Gu, Hongliang Ren, Fenglei Ni, Binghuang Cai, Yiwen Yang and Sandra Eriksson. Their work appears in journals such as IEEE Transactions on Industrial Informatics, Electronics Letters, IEEE Transactions on Industrial Electronics, Neurocomputing and Neural Networks.
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.