Robert J. Marks

297 papers receiving 6.4k citations

Robert J. Marks's Hit Papers

Electric load forecasting using an artificial neural network 1991 · 1.1k citations
1.1k0+11+23Years since publication2505007501000

Peers

Robert J. Marks
Comparison fields: 5 of 187
  • Signal Processing 633
  • Computer Vision and Pattern Recognition 1.1k
  • Artificial Intelligence 1.7k
  • Control and Systems Engineering 1.1k
  • Electrical and Electronic Engineering 2.6k
Replace Julia Abrahams with:
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Ming Li China
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Amir Beck Israel
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Åke Björck Sweden
Zongben Xu China
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Countries citing papers authored by Robert J. Marks

Since Specialization
Citations

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

Fields of papers citing papers by Robert J. Marks

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert J. Marks. 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 Robert J. Marks. The network helps show where Robert J. Marks may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 320 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Electric load forecasting using an artificial neural network
Hit paper breakdown →
19911115
2
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
1999433
3 1991350
4 1990307
5 1993195
6 2001145
7 2001130
8 2002119
9 1991107
10 2003105
11 199293
12 199086
13 199184
14 198182
15 200282
16 201481
17 199578
18 199878
19 199177
20 197868

About Robert J. Marks

Robert J. Marks is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Aerospace Engineering, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 320 papers that have together received 6.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (53 papers), Radar Systems and Signal Processing (47 papers), Advanced Power Amplifier Design (36 papers), Image and Signal Denoising Methods (33 papers), Radio Frequency Integrated Circuit Design (27 papers), Blind Source Separation Techniques (21 papers), Full-Duplex Wireless Communications (19 papers) and Mathematical Analysis and Transform Methods (18 papers). The work is most often cited by research in Signal Processing (633 citations), Computer Vision and Pattern Recognition (1.1k citations), Artificial Intelligence (1.7k citations), Control and Systems Engineering (1.1k citations) and Electrical and Electronic Engineering (2.6k citations). Robert J. Marks has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include M.A. El-Sharkawi, Les Atlas, Dong-Chul Park, Russell Reed, M.J. Damborg, Sung‐Kwun Oh, Charles Baylis, Payman Arabshahi, Yunxin Zhao and Craig A. Jensen. Their work appears in journals such as IEEE Transactions on Aerospace and Electronic Systems, Optics Letters, IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Power Systems and Journal of the Optical Society of America A.

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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