N. Boonsatit
Impact in
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- Neural Networks Stability and Synchronization
- Nonlinear Dynamics and Pattern Formation
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- stochastic dynamics and bifurcation
Papers in
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- Neural Networks Stability and Synchronization 10
- Nonlinear Dynamics and Pattern Formation 5
- Distributed Control Multi-Agent Systems 3
- Co-authors
- Grienggrai Rajchakit (15 shared papers)Chee Peng Lim (8 shared papers)Porpattama Hammachukiattikul (14 shared papers)R. Sriraman (5 shared papers)Praveen Agarwal (4 shared papers)Chutiphon Pukdeboon (1 shared paper)Anuwat Jirawattanapanit (7 shared papers)M. Syed Ali (2 shared papers)
In The Last Decade
N. Boonsatit
23 papers receiving 386 citations
Peers
Comparison fields: 5 of 53
- Computer Networks and Communications 192
- Statistical and Nonlinear Physics 76
- Modeling and Simulation 24
- Control and Systems Engineering 93
- Artificial Intelligence 101
Countries citing papers authored by N. Boonsatit
This map shows the geographic impact of N. Boonsatit'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 N. Boonsatit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites N. Boonsatit more than expected).
Fields of papers citing papers by N. Boonsatit
This network shows the impact of papers produced by N. Boonsatit. 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 N. Boonsatit. The network helps show where N. Boonsatit may publish in the future.
Co-authors
The 25 scholars most cited alongside N. Boonsatit, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 72 | |
| 2 | 2021 | 52 | |
| 3 | 2021 | 51 | |
| 4 | 2022 | 49 | |
| 5 | 2021 | 34 | |
| 6 | 2016 | 28 | |
| 7 | 2021 | 21 | |
| 8 | 2021 | 19 | |
| 9 | 2022 | 12 | |
| 10 | 2022 | 11 | |
| 11 | 2021 | 9 | |
| 12 | 2022 | 8 | |
| 13 | 2021 | 6 | |
| 14 | 2022 | 4 | |
| 15 | 2022 | 3 | |
| 16 | 2022 | 3 | |
| 17 | 2022 | 3 | |
| 18 | 2022 | 3 | |
| 19 | 2021 | 3 | |
| 20 | 2021 | 2 |
About N. Boonsatit
N. Boonsatit is a scholar working on Computer Networks and Communications, Control and Systems Engineering, Electrical and Electronic Engineering, Computational Mechanics and Management Science and Operations Research, having authored 23 papers that have together received 397 indexed citations. Recurring topics across this work include Neural Networks Stability and Synchronization (10 papers), Nonlinear Dynamics and Pattern Formation (5 papers), Multi-Criteria Decision Making (4 papers), Nanofluid Flow and Heat Transfer (4 papers), Advanced Memory and Neural Computing (3 papers), Distributed Control Multi-Agent Systems (3 papers), Fluid Dynamics and Turbulent Flows (3 papers) and Fuzzy and Soft Set Theory (3 papers). The work is most often cited by research in Computer Networks and Communications (192 citations), Statistical and Nonlinear Physics (76 citations), Modeling and Simulation (24 citations), Control and Systems Engineering (93 citations) and Artificial Intelligence (101 citations). N. Boonsatit has collaborated with scholars based in Thailand, India and Australia. Frequent co-authors include Grienggrai Rajchakit, Chee Peng Lim, Porpattama Hammachukiattikul, R. Sriraman, Praveen Agarwal, Chutiphon Pukdeboon, Anuwat Jirawattanapanit, M. Syed Ali, Umair Khan and Zehba Raizah. Their work appears in journals such as Advances in Difference Equations, IEEE Access, Complexity, Lubricants and Frontiers in Chemistry.
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.