Michael Fairbank

946 total citations
28 papers, 651 citations indexed

About

Michael Fairbank is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Michael Fairbank has authored 28 papers receiving a total of 651 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 12 papers in Control and Systems Engineering and 12 papers in Computational Theory and Mathematics. Recurrent topics in Michael Fairbank's work include Adaptive Dynamic Programming Control (11 papers), Reinforcement Learning in Robotics (8 papers) and Microgrid Control and Optimization (7 papers). Michael Fairbank is often cited by papers focused on Adaptive Dynamic Programming Control (11 papers), Reinforcement Learning in Robotics (8 papers) and Microgrid Control and Optimization (7 papers). Michael Fairbank collaborates with scholars based in United Kingdom, United States and Switzerland. Michael Fairbank's co-authors include Eduardo Alonso, Shuhui Li, Donald C. Wunsch, Xingang Fu, Danil Prokhorov, Hoyun Won, Zhongwen Li, Sheri M. Markose, Yang Xiao and Bing Lu and has published in prestigious journals such as European Journal of Operational Research, IEEE Transactions on Cybernetics and Neural Computation.

In The Last Decade

Michael Fairbank

25 papers receiving 635 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Fairbank United Kingdom 13 381 341 158 151 56 28 651
Nurettin Çetinkaya Türkiye 10 323 0.8× 183 0.5× 95 0.6× 209 1.4× 36 0.6× 29 614
S. Prabhakar Karthikeyan India 15 674 1.8× 234 0.7× 37 0.2× 101 0.7× 81 1.4× 106 967
John G. Vlachogiannis Greece 16 991 2.6× 423 1.2× 63 0.4× 164 1.1× 77 1.4× 37 1.2k
Ayyarao S. L. V. Tummala India 11 263 0.7× 172 0.5× 81 0.5× 267 1.8× 43 0.8× 24 603
Ali Djerioui Algeria 16 377 1.0× 480 1.4× 50 0.3× 81 0.5× 118 2.1× 52 710
Ramesh Devarapalli India 15 509 1.3× 321 0.9× 83 0.5× 166 1.1× 24 0.4× 65 761
Belkacem Mahdad Algeria 19 1.1k 2.8× 439 1.3× 66 0.4× 170 1.1× 15 0.3× 87 1.2k
Tarek Bouktir Algeria 15 607 1.6× 304 0.9× 32 0.2× 100 0.7× 21 0.4× 47 757
Zijian Hu China 12 261 0.7× 159 0.5× 27 0.2× 160 1.1× 56 1.0× 44 639
Jiajun Wang China 14 176 0.5× 406 1.2× 34 0.2× 155 1.0× 32 0.6× 51 659

Countries citing papers authored by Michael Fairbank

Since Specialization
Citations

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

Fields of papers citing papers by Michael Fairbank

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Fairbank. 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 Fairbank. The network helps show where Michael Fairbank may publish in the future.

Co-authorship network of co-authors of Michael Fairbank

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Fairbank. A scholar is included among the top collaborators of Michael Fairbank based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Fairbank. Michael Fairbank is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fairbank, Michael, et al.. (2023). A Minimal “Functionally Sentient” Organism Trained With Backpropagation Through Time. Adaptive Behavior. 31(6). 531–544. 1 indexed citations
2.
Yang, Xinan, et al.. (2023). Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders. European Journal of Operational Research. 309(2). 704–718. 16 indexed citations
3.
Li, Shuhui, et al.. (2022). An Iterative Optimization and Learning-Based IoT System for Energy Management of Connected Buildings. IEEE Internet of Things Journal. 9(21). 21246–21259. 27 indexed citations
4.
Li, Shuhui, et al.. (2021). Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks. IEEE Transactions on Circuits and Systems I Regular Papers. 68(4). 1760–1768. 70 indexed citations
5.
Fairbank, Michael, et al.. (2020). Practical Game Design Tool: State Explorer. 439–446.
6.
Krause, Andreas & Michael Fairbank. (2020). Baseline win rates for neural-network based trading algorithms. Pure (University of Bath). 1–6. 2 indexed citations
7.
Li, Shuhui, Hoyun Won, Xingang Fu, et al.. (2019). Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results. IEEE Transactions on Cybernetics. 50(7). 3218–3230. 79 indexed citations
8.
Fairbank, Michael, et al.. (2019). Extracting Learning Curves From Puzzle Games. 150–155. 4 indexed citations
9.
Fairbank, Michael, et al.. (2017). Convolutional neural networks applied to high-frequency market microstructure forecasting. 31–36. 19 indexed citations
10.
Samothrakis, Spyridon, et al.. (2017). Convolutional-Match Networks for Question Answering. 2686–2692. 3 indexed citations
11.
Samothrakis, Spyridon, et al.. (2016). Match memory recurrent networks. abs 1502 5698. 1339–1346. 1 indexed citations
12.
Li, Shuhui, Xingang Fu, Eduardo Alonso, Michael Fairbank, & Donald C. Wunsch. (2015). Neural-network based vector control of VSCHVDC transmission systems. City Research Online (City University London). 25. 173–180. 1 indexed citations
13.
Alonso, Eduardo, Michael Fairbank, & Esther Mondragón. (2015). Back to optimality: a formal framework to express the dynamics of learning optimal behavior. Adaptive Behavior. 23(4). 206–215.
14.
Fu, Xingang, Shuhui Li, Michael Fairbank, Donald C. Wunsch, & Eduardo Alonso. (2014). Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter. IEEE Transactions on Neural Networks and Learning Systems. 26(9). 1900–1912. 114 indexed citations
15.
Fairbank, Michael, Shuhui Li, Xingang Fu, Eduardo Alonso, & Donald C. Wunsch. (2013). An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances. Neural Networks. 49. 74–86. 31 indexed citations
16.
Li, Shuhui, Michael Fairbank, Xingang Fu, Donald C. Wunsch, & Eduardo Alonso. (2013). Nested-loop neural network vector control of permanent magnet synchronous motors. 14. 1–8. 4 indexed citations
17.
Alonso, Eduardo & Michael Fairbank. (2013). Emergent and Adaptive Systems of Systems. 1721–1725. 2 indexed citations
18.
Fairbank, Michael, Eduardo Alonso, & Danil Prokhorov. (2013). An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time. IEEE Transactions on Neural Networks and Learning Systems. 24(12). 2088–2100. 28 indexed citations
19.
Li, Shuhui, Donald C. Wunsch, Michael Fairbank, & Eduardo Alonso. (2012). Vector control of a grid-connected rectifier/inverter using an artificial neural network. City Research Online (City University London). 1–7. 22 indexed citations
20.
Fairbank, Michael, Eduardo Alonso, & Danil Prokhorov. (2012). Simple and Fast Calculation of the Second-Order Gradients for Globalized Dual Heuristic Dynamic Programming in Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 23(10). 1671–1676. 35 indexed citations

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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