Daniel Sbárbaro

5.0k citations
177 papers · 3.6k indexed · 1 hit paper · h-index 24

Daniel Sbárbaro

168 papers receiving 3.4k citations

Hit Papers

Neural networks for control systems—A survey1.3k19922026200320144008001.2k

Peers

Daniel Sbárbaro
Comparison fields: 5 of 137
  • Control and Systems Engineering 2.4k
  • Artificial Intelligence 1.1k
  • Analytical Chemistry 205
  • Statistical and Nonlinear Physics 193
  • Mechanical Engineering 481
Replace Nina F. Thornhill with:
Nina F. Thornhill United Kingdom
Shankar Narasimhan India
Bjarne Foss Norway
Junghui Chen Taiwan
Prashant Mhaskar Canada
Qinghua Zhang France
Zhe Wu United States
B. Erik Ydstie United States
Xinggao Liu China
Zhenyu Huang United States
Daniel Sbárbaro relative to Nina F. Thornhill United Kingdom Nina F. Thornhill's profile →
Citations per field
00.5×1.5×2.2×
Nina F. Thornhill · 1×
Citations per year

Countries citing papers authored by Daniel Sbárbaro

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Sbárbaro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20240
3 20241
4 20240
5 20223
6 202210
7 202132
8 202134
9 20216
10 20173
11 201628
12 20117
13 20063
14 20067
15 200418
16 200323
17 20033
18 200223
19 19981
20
Stochastic Approximation and Multilayer Perceptrons: The Gain Backpropagation Algorithm.
19906

About Daniel Sbárbaro

Daniel Sbárbaro is a scholar working on Control and Systems Engineering, Analytical Chemistry, Artificial Intelligence, Renewable Energy, Sustainability and the Environment and Fluid Flow and Transfer Processes, having authored 177 papers that have together received 3.6k indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (53 papers), Fault Detection and Control Systems (32 papers), Control Systems and Identification (27 papers), Iterative Learning Control Systems (19 papers), Control and Stability of Dynamical Systems (17 papers), Neural Networks and Applications (17 papers), Advanced Control Systems Design (15 papers) and Multilevel Inverters and Converters (15 papers). The work is most often cited by research in Control and Systems Engineering (2.4k citations), Artificial Intelligence (1.1k citations), Analytical Chemistry (205 citations), Statistical and Nonlinear Physics (193 citations) and Mechanical Engineering (481 citations). Daniel Sbárbaro has collaborated with scholars based in Chile, France and United Kingdom. Frequent co-authors include Kate Hunt, P.J. Gawthrop, R. Żbikowski, Kenneth J. Hunt, Héctor Ramírez, Bernhard Maschke, José Espinoza, L. Morán, Sergio Torres and J.M. Gomes da Silva. Their work appears in journals such as IEEE Transactions on Control Systems Technology, IEEE Transactions on Industrial Electronics, Journal of Process Control, IEEE Transactions on Industry Applications and Journal of Analytical Atomic Spectrometry.

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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2026