Hao‐Chin Chang

488 citations
37 papers · 399 indexed · h-index 10

Hao‐Chin Chang

34 papers receiving 392 citations

Peers

Hao‐Chin Chang
Comparison fields: 5 of 44
  • Industrial and Manufacturing Engineering 167
  • Control and Systems Engineering 169
  • Computer Networks and Communications 121
  • Artificial Intelligence 60
  • Computational Theory and Mathematics 22
Replace Xiaolong Chen with:
Xiaolong Chen China
K.K. Schwarz United States
Marco Stolpe Germany
K. Kompella India
Sebastian Engell Germany
Markus Rentschler Germany
P.J. Gyugyi United States
Xuefei Yang China
János Farkas Hungary
Graham McMahon Australia
Hao‐Chin Chang relative to Xiaolong Chen China Xiaolong Chen's profile →
Citations per field
00.5×10×14.7×
Xiaolong Chen · 1×
Citations per year

Countries citing papers authored by Hao‐Chin Chang

Since Specialization
Citations

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

Fields of papers citing papers by Hao‐Chin Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hao‐Chin Chang. 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 Hao‐Chin Chang. The network helps show where Hao‐Chin Chang may publish in the future.

Co-authorship network

The 23 scholars most cited alongside Hao‐Chin Chang, 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 Hao‐Chin Chang Line = papers co-authored together Hao‐Chin Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20251
3 20250
4 20232
5 20225
6 20214
7 20211
8 20205
9 20196
10 201813
11 20184
12 20189
13 201710
14 20146
15 201413
16 201214
17 201039
18 20101
19 20044
20 200110

About Hao‐Chin Chang

Hao‐Chin Chang is a scholar working on Control and Systems Engineering, Computer Networks and Communications and Energy Engineering and Power Technology, having authored 37 papers that have together received 399 indexed citations. Recurring topics across this work include Stability and Control of Uncertain Systems (22 papers), Neural Networks Stability and Synchronization (18 papers), Adaptive Control of Nonlinear Systems (7 papers), Control Systems and Identification (7 papers), Advanced Control Systems Design (5 papers), Stability and Controllability of Differential Equations (4 papers), Advanced Manufacturing and Logistics Optimization (3 papers) and Assembly Line Balancing Optimization (3 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (167 citations), Control and Systems Engineering (169 citations) and Computer Networks and Communications (121 citations). Hao‐Chin Chang has collaborated with scholars based in Taiwan, Yemen and India. Frequent co-authors include Tung-Kuan Liu, Chang‐Hua Lien, Ker‐Wei Yu, Jyh‐Horng Chou, Jenq-Der Chen, Yi‐You Hou, Chien‐Yu Huang, Long‐Yeu Chung, Yuan-Chih Chang and Berlin Chen. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Journal of the Franklin Institute.

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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