Min Wu

29.4k total citations · 11 hit papers
883 papers, 22.6k citations indexed

About

Min Wu is a scholar working on Control and Systems Engineering, Mechanical Engineering and Computer Networks and Communications. According to data from OpenAlex, Min Wu has authored 883 papers receiving a total of 22.6k indexed citations (citations by other indexed papers that have themselves been cited), including 492 papers in Control and Systems Engineering, 200 papers in Mechanical Engineering and 192 papers in Computer Networks and Communications. Recurrent topics in Min Wu's work include Stability and Control of Uncertain Systems (178 papers), Neural Networks Stability and Synchronization (138 papers) and Fault Detection and Control Systems (96 papers). Min Wu is often cited by papers focused on Stability and Control of Uncertain Systems (178 papers), Neural Networks Stability and Synchronization (138 papers) and Fault Detection and Control Systems (96 papers). Min Wu collaborates with scholars based in China, Japan and United Kingdom. Min Wu's co-authors include Yong He, Jinhua She, Chuan‐Ke Zhang, Lin Jiang, Shuai Liu, Weihua Cao, Hong‐Bing Zeng, Qing‐Guo Wang, Luefeng Chen and Xuzhi Lai and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and IEEE Transactions on Automatic Control.

In The Last Decade

Min Wu

814 papers receiving 22.1k citations

Hit Papers

Delay-dependent criteria ... 2003 2026 2010 2018 2004 2006 2015 2003 2004 250 500 750

Author Peers

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

Author Last Decade Papers Cites
Min Wu 14.6k 10.0k 4.0k 3.0k 3.0k 883 22.6k
Hamid Reza Karimi 21.0k 1.4× 10.3k 1.0× 3.4k 0.8× 2.7k 0.9× 3.2k 1.1× 956 30.6k
Huijun Gao 30.5k 2.1× 17.8k 1.8× 3.4k 0.8× 4.0k 1.3× 5.2k 1.7× 621 43.6k
Xinping Guan 9.3k 0.6× 9.6k 1.0× 5.4k 1.3× 1.6k 0.5× 2.4k 0.8× 994 20.2k
Ligang Wu 19.3k 1.3× 8.6k 0.9× 3.2k 0.8× 2.1k 0.7× 3.0k 1.0× 417 23.7k
Hongye Su 14.8k 1.0× 9.3k 0.9× 3.2k 0.8× 3.0k 1.0× 2.0k 0.7× 713 20.8k
Huaguang Zhang 14.9k 1.0× 10.7k 1.1× 6.0k 1.5× 7.9k 2.6× 5.9k 1.9× 833 26.0k
Lihua Xie 20.8k 1.4× 17.6k 1.8× 8.2k 2.0× 2.9k 1.0× 5.9k 1.9× 1.1k 40.8k
Miroslav Krstić 39.6k 2.7× 7.9k 0.8× 3.6k 0.9× 9.2k 3.0× 1.6k 0.5× 936 46.1k
Jean-Jacques Slotine 18.9k 1.3× 5.0k 0.5× 3.1k 0.8× 1.5k 0.5× 3.3k 1.1× 177 29.1k
Fuad E. Alsaadi 7.5k 0.5× 8.8k 0.9× 2.9k 0.7× 1.3k 0.4× 4.7k 1.5× 443 19.1k

Countries citing papers authored by Min Wu

Since Specialization
Citations

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

Fields of papers citing papers by Min Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min Wu. 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 Min Wu. The network helps show where Min Wu may publish in the future.

Co-authorship network of co-authors of Min Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Min Wu. A scholar is included among the top collaborators of Min Wu 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 Min Wu. Min Wu 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
2.
Cao, Weihua, et al.. (2025). Knowledge-driven dynamic multi-objective evaluation and attention optimization for tuning microwave filters. Applied Soft Computing. 171. 112702–112702. 1 indexed citations
3.
Xu, Yan, et al.. (2025). Unit load prediction method based on weighted just-in-time learning with spatio-temporal characteristics for gas boiler power generation process. Control Engineering Practice. 160. 106325–106325. 1 indexed citations
4.
Du, Sheng, et al.. (2024). Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review. Computers in Industry. 165. 104215–104215. 1 indexed citations
5.
Yang, Xiao, et al.. (2024). Prediction of rate of penetration based on drilling conditions identification for drilling process. Neurocomputing. 579. 127439–127439. 10 indexed citations
6.
Wu, Min, et al.. (2024). Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis. Journal of Process Control. 142. 103284–103284. 2 indexed citations
7.
Meng, Qingxin, Xuzhi Lai, Yawu Wang, et al.. (2024). Design, performance analysis and applications of pneumatic bellows actuator for building block soft robots. Information Sciences. 676. 120814–120814. 3 indexed citations
8.
Zhang, Pan, et al.. (2024). Cooperative control of multiple magnetically controlled soft robots. Information Sciences. 677. 120790–120790.
9.
Chen, Luefeng, et al.. (2024). Improved ShuffleNet V2 network with attention for speech emotion recognition. Information Sciences. 689. 121488–121488. 3 indexed citations
10.
Hu, Jie, et al.. (2024). Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process. Control Engineering Practice. 145. 105850–105850. 4 indexed citations
11.
Chen, Luefeng, et al.. (2024). Lithology identification of coal-bearing strata based on data-driven dual-channel relevance networks in coal mine roadway drilling process. Information Sciences. 690. 121339–121339. 9 indexed citations
12.
He, Yong, et al.. (2024). Delay-variation-dependent summation inequality and its application to stability analysis of discrete-time systems with time-varying delay. Systems & Control Letters. 184. 105721–105721. 13 indexed citations
13.
Chen, Luefeng, et al.. (2024). Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation. IEEE Transactions on Artificial Intelligence. 5(12). 6500–6508. 1 indexed citations
14.
Lai, Xuzhi, et al.. (2024). Improvement of Rate of Penetration in Drilling Process Based on TCN-Vibration Recognition. IEEE Transactions on Instrumentation and Measurement. 73. 1–12. 2 indexed citations
15.
Gan, Chao, Xiang Wang, Weihua Cao, et al.. (2023). Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process. Geoenergy Science and Engineering. 230. 212187–212187. 11 indexed citations
16.
Lai, Xuzhi, et al.. (2023). Modeling, analysis, and adaptive neural modified-backstepping control of an uncertain horizontal pendubot with double flexible joints. Control Engineering Practice. 139. 105647–105647. 6 indexed citations
17.
Zhang, Chuan‐Ke, et al.. (2023). Stability analysis of discrete-time systems with time-varying delay via a delay-dependent matrix-separation-based inequality. Automatica. 156. 111192–111192. 46 indexed citations
18.
Liu, Kang‐Zhi, et al.. (2023). Harmonic Disturbance Suppression for High-Performance Nonlinear Repetitive-Control Systems. IFAC-PapersOnLine. 56(2). 4545–4550. 3 indexed citations
19.
Wu, Min, et al.. (2023). Fault diagnosis of drilling process based on multi-scale decomposition and decision fusion. IFAC-PapersOnLine. 56(2). 8079–8084. 1 indexed citations
20.
Wu, Min, Michio Nakano, & Jinhua She. (1997). A Model-Based Expert System for Determining Optimal Control Inputs in Purification Process Control. IEEJ Transactions on Industry Applications. 117(7). 856–862. 1 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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