Michael Hüsken
Impact in
- Signal Processing top 5%
- Time Series Analysis and Forecasting
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Anomaly Detection Techniques and Applications
Papers in
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- Neural Networks and Applications 7
- Evolutionary Algorithms and Applications 4
- Metaheuristic Optimization Algorithms Research 4
- Machine Learning and Data Classification 1
- Machine Learning and ELM 1
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- Model Reduction and Neural Networks 2
- Co-authors
- Christian Igel (4 shared papers)Peter Stagge (2 shared papers)C. von der Malsburg (1 shared paper)Stefan Gehlen (1 shared paper)Marc Toussaint (1 shared paper)Christian Goerick (1 shared paper)Bernhard Sendhoff (1 shared paper)Jörn Mehnen (1 shared paper)
- Journals
- Neurocomputing (2 papers)Zeitschrift für wirtschaftlichen Fabrikbetrieb (1 paper)Connection Science (1 paper)Genetic and Evolutionary Computation Conference (1 paper)
In The Last Decade
Michael Hüsken
9 papers receiving 844 citations
Peers
Comparison fields: 5 of 120
- Signal Processing 187
- Artificial Intelligence 424
- Computer Vision and Pattern Recognition 226
- Management Science and Operations Research 50
- Control and Systems Engineering 84
Countries citing papers authored by Michael Hüsken
This map shows the geographic impact of Michael Hüsken'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 Michael Hüsken with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Hüsken more than expected).
Fields of papers citing papers by Michael Hüsken
This network shows the impact of papers produced by Michael Hüsken. 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 Michael Hüsken. The network helps show where Michael Hüsken may publish in the future.
Co-authors
The 9 scholars most cited alongside Michael Hüsken, 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 | 2003 | 307 | |
| 2 | 2003 | 250 | |
| 3 | Improving the Rprop Learning Algorithm | 2000 | 232 |
| 4 | 2006 | 90 | |
| 5 | 2002 | 17 | |
| 6 | 2002 | 12 | |
| 7 | 2000 | 11 | |
| 8 | Balancing learning and evolution | 2002 | 5 |
| 9 | 2001 | 1 |
About Michael Hüsken
Michael Hüsken is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 925 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Evolutionary Algorithms and Applications (4 papers), Metaheuristic Optimization Algorithms Research (4 papers), Model Reduction and Neural Networks (2 papers), Machine Learning and Data Classification (1 paper), Blind Source Separation Techniques (1 paper), Machine Learning and ELM (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Signal Processing (187 citations), Artificial Intelligence (424 citations), Computer Vision and Pattern Recognition (226 citations), Management Science and Operations Research (50 citations) and Control and Systems Engineering (84 citations). Michael Hüsken has collaborated with scholars based in Germany and Norway. Frequent co-authors include Christian Igel, Peter Stagge, C. von der Malsburg, Stefan Gehlen, Marc Toussaint, Christian Goerick, Bernhard Sendhoff, Jörn Mehnen and Klaus Weinert. Their work appears in journals such as Neurocomputing, Zeitschrift für wirtschaftlichen Fabrikbetrieb, Connection Science and Genetic and Evolutionary Computation Conference.
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.