Jun Zhang

278 papers receiving 4.0k citations

Jun Zhang's Hit Papers

Wavelet neural networks for function learning 1995 · 542 citations
5420+10+20Years since publication100200300400500

Peers

Jun Zhang
Comparison fields: 5 of 141
  • Control and Systems Engineering 1.6k
  • Atomic and Molecular Physics, and Optics 2.0k
  • Aerospace Engineering 1.2k
  • Computer Vision and Pattern Recognition 625
  • Electrical and Electronic Engineering 1.6k
Replace G. Schmidt with:
G. Schmidt Germany
Jun Tang China
Patrick Y. Hwang United States
Hong Guo China
F.J. Harris United States
Denis Zorin United States
H.J. Trussell United States
Robert Weigel Germany
Xinlong Wang China
Bruno Andò Italy
Jun Zhang relative to G. Schmidt Germany G. Schmidt's profile →
Citations per field
00.5×10×
G. Schmidt · 1×
Citations per year

Countries citing papers authored by Jun Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Jun Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 302 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Wavelet neural networks for function learning
Hit paper breakdown →
1995542
2 2011191
3 2005141
4 200299
5 202091
6 202288
7 202082
8 202075
9 201071
10 201669
11 200962
12 201860
13 202258
14 201056
15 200853
16 201251
17 200750
18 202150
19 200849
20 201041

About Jun Zhang

Jun Zhang is a scholar working on Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Aerospace Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 302 papers that have together received 4.1k indexed citations. Recurring topics across this work include Gyrotron and Vacuum Electronics Research (120 papers), Pulsed Power Technology Applications (91 papers), Microwave Engineering and Waveguides (69 papers), Advanced SAR Imaging Techniques (38 papers), Particle accelerators and beam dynamics (37 papers), Radar Systems and Signal Processing (19 papers), Robotics and Sensor-Based Localization (14 papers) and Advanced Neural Network Applications (12 papers). The work is most often cited by research in Control and Systems Engineering (1.6k citations), Atomic and Molecular Physics, and Optics (2.0k citations), Aerospace Engineering (1.2k citations), Computer Vision and Pattern Recognition (625 citations) and Electrical and Electronic Engineering (1.6k citations). Jun Zhang has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Huihuang Zhong, G.G. Walter, Ting Shu, Zhenxing Jin, Ronghui Zhan, Wei Wang, Dian Zhang, Bao-Liang Qian, Yuwei Fan and Xingjun Ge. Their work appears in journals such as Physics of Plasmas, IEEE Transactions on Electron Devices, IEEE Transactions on Plasma Science, AIP Advances and Review of Scientific Instruments.

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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