Chao-Chee Ku
Impact in
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- Adaptive Control of Nonlinear Systems
- Iterative Learning Control Systems
- Advanced Algorithms and Applications
- Fault Detection and Control Systems
- Advanced Control Systems Optimization
- Control Systems and Identification
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
Papers in
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- Neural Networks and Applications 6
- Machine Learning and ELM 1
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- Video Coding and Compression Technologies 2
- Blind Source Separation Techniques 2
- Co-authors
- K.Y. Lee (4 shared papers)Kwang Y. Lee (1 shared paper)June Ho Park (1 shared paper)R.M. Edwards (1 shared paper)
- Journals
- IEEE Transactions on Consumer Electronics (3 papers)IEEE Transactions on Neural Networks (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Chao-Chee Ku
8 papers receiving 576 citations
Chao-Chee Ku's Hit Papers
Peers
Comparison fields: 5 of 57
- Control and Systems Engineering 440
- Artificial Intelligence 307
- Signal Processing 33
- Electrical and Electronic Engineering 124
- Energy Engineering and Power Technology 6
Countries citing papers authored by Chao-Chee Ku
This map shows the geographic impact of Chao-Chee Ku'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 Chao-Chee Ku with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chao-Chee Ku more than expected).
Fields of papers citing papers by Chao-Chee Ku
This network shows the impact of papers produced by Chao-Chee Ku. 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 Chao-Chee Ku. The network helps show where Chao-Chee Ku may publish in the future.
Co-authors
The 4 scholars most cited alongside Chao-Chee Ku, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Diagonal recurrent neural networks for dynamic systems control Hit paper breakdown → | 1995 | 564 |
| 2 | 1992 | 17 | |
| 3 | 2004 | 6 | |
| 4 | 2005 | 5 | |
| 5 | 2004 | 5 | |
| 6 | 2002 | 4 | |
| 7 | 2005 | 3 | |
| 8 | Diagonal recurrent neural networks for control of dynamic systems | 1993 | 1 |
| 9 | 2002 | 0 |
About Chao-Chee Ku
Chao-Chee Ku is a scholar working on Artificial Intelligence, Signal Processing, Control and Systems Engineering, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 9 papers that have together received 605 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Advanced Image Processing Techniques (3 papers), Fault Detection and Control Systems (2 papers), Video Coding and Compression Technologies (2 papers), Blind Source Separation Techniques (2 papers), Advanced Vision and Imaging (2 papers), Machine Learning and ELM (1 paper) and Nuclear reactor physics and engineering (1 paper). The work is most often cited by research in Control and Systems Engineering (440 citations), Artificial Intelligence (307 citations), Signal Processing (33 citations), Electrical and Electronic Engineering (124 citations) and Energy Engineering and Power Technology (6 citations). Chao-Chee Ku has collaborated with scholars based in United States and South Korea. Frequent co-authors include K.Y. Lee, Kwang Y. Lee, June Ho Park and R.M. Edwards. Their work appears in journals such as IEEE Transactions on Consumer Electronics and IEEE Transactions on Neural Networks.
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.