Jin Liang

4.9k citations
211 papers · 3.8k · h-index 31

Impact in

Papers in

Jin Liang

193 papers receiving 3.4k citations

Peers

Jin Liang
Comparison fields: 5 of 113
  • Applied Mathematics 2.3k
  • Modeling and Simulation 934
  • Numerical Analysis 676
  • Control and Systems Engineering 1.7k
  • Mathematical Physics 585
Replace Jerzy Klamka with:
Jerzy Klamka Poland
Xinguang Zhang China
Hans Zwart Netherlands
Asen L. Dontchev United States
Tadeusz Kaczorek Poland
Richard Vinter United Kingdom
Peter R. Wolenski United States
M. ‎Mursaleen India
E. Babolian Iran
S. A. Yousefi Iran
Jin Liang relative to Jerzy Klamka Poland Jerzy Klamka's profile →
Citations per field
00.5×5.4×
Jerzy Klamka · 1×
Citations per year

Countries citing papers authored by Jin Liang

Since Specialization
Citations

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

Fields of papers citing papers by Jin Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 211 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
2003425
2 2007144
3 2007139
4 2012120
5 2008112
6 1998110
7 2004110
8 2015105
9 199589
10 200883
11 201482
12 201481
13 201376
14 200869
15 200465
16 200561
17 201560
18 201156
19 201255
20 200254

About Jin Liang

Jin Liang is a scholar working on Applied Mathematics, Control and Systems Engineering, Computational Theory and Mathematics, Numerical Analysis and Mathematical Physics, having authored 211 papers that have together received 3.8k indexed citations. Recurring topics across this work include Nonlinear Differential Equations Analysis (97 papers), Stability and Controllability of Differential Equations (71 papers), Advanced Mathematical Modeling in Engineering (44 papers), Differential Equations and Boundary Problems (40 papers), Differential Equations and Numerical Methods (36 papers), Fractional Differential Equations Solutions (25 papers), Advanced Mathematical Physics Problems (22 papers) and Fixed Point Theorems Analysis (17 papers). The work is most often cited by research in Applied Mathematics (2.3k citations), Modeling and Simulation (934 citations), Numerical Analysis (676 citations), Control and Systems Engineering (1.7k citations) and Mathematical Physics (585 citations). Jin Liang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Ti‐Jun Xiao, Madan M. Gupta, Noriyasu Homma, James H. Liu, Hui-Sheng Ding, Wei Huang, Li Yan, P.N. Nikiforuk, Jun Zhang and Μ.Μ. Gupta. Their work appears in journals such as Nonlinear Analysis, Journal of Mathematical Analysis and Applications, Advances in Difference Equations, Computers & Mathematics with Applications and Journal of Differential Equations.

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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