Michael A. Lehr
Impact in
- Artificial Intelligence top 1%
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Signal Processing top 2%
- Blind Source Separation Techniques
Papers in
-
- Neural Networks and Applications 6
- Stochastic Gradient Optimization Techniques 1
- Fuzzy Logic and Control Systems 1
- Neural Networks and Reservoir Computing 1
-
- Blind Source Separation Techniques 2
- Co-authors
- Bernard Widrow (9 shared papers)David E. Rumelhart (2 shared papers)Eric A. Wan (1 shared paper)Françoise Beaufays (1 shared paper)
- Journals
- Communications of the ACM (2 papers)International Journal of Intelligent Systems (1 paper)Proceedings of the IEEE (1 paper)The Journal of the Acoustical Society of America (1 paper)IEEE International Conference on Neural Networks (1 paper)
- Partner nations
- United States
In The Last Decade
Michael A. Lehr
9 papers receiving 2.0k citations
Michael A. Lehr's Hit Papers
Peers
Comparison fields: 5 of 169
- Artificial Intelligence 1.1k
- Signal Processing 254
- Control and Systems Engineering 462
- Computer Vision and Pattern Recognition 242
- Management Science and Operations Research 123
Countries citing papers authored by Michael A. Lehr
This map shows the geographic impact of Michael A. Lehr'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 A. Lehr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael A. Lehr more than expected).
Fields of papers citing papers by Michael A. Lehr
This network shows the impact of papers produced by Michael A. Lehr. 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 A. Lehr. The network helps show where Michael A. Lehr may publish in the future.
Co-authors
The 4 scholars most cited alongside Michael A. Lehr, 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 | 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation Hit paper breakdown → | 1990 | 1568 |
| 2 | 1994 | 331 | |
| 3 | 1994 | 312 | |
| 4 | 1993 | 21 | |
| 5 | Perceptrons, adalines, and backpropagation | 1998 | 20 |
| 6 | Noise canceling and channel equalization | 1998 | 5 |
| 7 | 2002 | 4 | |
| 8 | Scaled stochastic methods for training neural networks | 1996 | 2 |
| 9 | Commercial and Industrial Applications of Neural Networks | 1993 | 1 |
| 10 | 1999 | 0 |
About Michael A. Lehr
Michael A. Lehr is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computational Mechanics, having authored 10 papers that have together received 2.3k indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Blind Source Separation Techniques (2 papers), Statistical Mechanics and Entropy (2 papers), Advanced Adaptive Filtering Techniques (2 papers), Stochastic Gradient Optimization Techniques (1 paper), Fuzzy Logic and Control Systems (1 paper), Neural Networks and Reservoir Computing (1 paper) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Artificial Intelligence (1.1k citations), Signal Processing (254 citations), Control and Systems Engineering (462 citations), Computer Vision and Pattern Recognition (242 citations) and Management Science and Operations Research (123 citations). Michael A. Lehr has collaborated with scholars based in United States. Frequent co-authors include Bernard Widrow, David E. Rumelhart, Eric A. Wan and Françoise Beaufays. Their work appears in journals such as Communications of the ACM, International Journal of Intelligent Systems, Proceedings of the IEEE, The Journal of the Acoustical Society of America and IEEE International Conference 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.