Fernando Gomide

9.8k total citations · 3 hit papers
220 papers, 6.6k citations indexed

About

Fernando Gomide is a scholar working on Artificial Intelligence, Control and Systems Engineering and Management Science and Operations Research. According to data from OpenAlex, Fernando Gomide has authored 220 papers receiving a total of 6.6k indexed citations (citations by other indexed papers that have themselves been cited), including 150 papers in Artificial Intelligence, 47 papers in Control and Systems Engineering and 43 papers in Management Science and Operations Research. Recurrent topics in Fernando Gomide's work include Fuzzy Logic and Control Systems (110 papers), Neural Networks and Applications (97 papers) and Stock Market Forecasting Methods (29 papers). Fernando Gomide is often cited by papers focused on Fuzzy Logic and Control Systems (110 papers), Neural Networks and Applications (97 papers) and Stock Market Forecasting Methods (29 papers). Fernando Gomide collaborates with scholars based in Brazil, United States and Slovenia. Fernando Gomide's co-authors include Witold Pedrycz, Rosângela Ballini, Daniel Leite, André Lemos, Walmir M. Caminhas, Francisco Herrera, Óscar Cordón, Frank Hoffmann, Luis Magdalena and Leandro Maciel and has published in prestigious journals such as IEEE Transactions on Automatic Control, Expert Systems with Applications and Information Sciences.

In The Last Decade

Fernando Gomide

205 papers receiving 6.3k citations

Hit Papers

Uncertain rule-based fuzz... 1998 2026 2007 2016 2002 1998 2003 250 500 750 1000

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Fernando Gomide 4.2k 1.4k 1.3k 902 868 220 6.6k
Rudolf Kruse 3.6k 0.8× 898 0.7× 1.2k 0.9× 861 1.0× 897 1.0× 224 6.0k
Bo Yuan 2.4k 0.6× 861 0.6× 2.1k 1.7× 1.2k 1.3× 861 1.0× 79 6.4k
Dimitar Filev 3.7k 0.9× 2.9k 2.2× 2.3k 1.8× 856 0.9× 1.2k 1.4× 210 8.5k
Óscar Cordón 4.9k 1.2× 748 0.6× 951 0.8× 1.0k 1.1× 571 0.7× 233 7.8k
Edurne Barrenechea 3.6k 0.8× 415 0.3× 1.9k 1.5× 1.3k 1.4× 868 1.0× 91 6.4k
Plamen Angelov 5.6k 1.3× 2.1k 1.5× 731 0.6× 383 0.4× 661 0.8× 307 8.9k
Luciano Sánchez 4.0k 1.0× 414 0.3× 563 0.4× 876 1.0× 519 0.6× 160 5.8k
Edwin Lughofer 4.0k 1.0× 1.5k 1.1× 457 0.4× 281 0.3× 405 0.5× 211 5.8k
Abraham Kandel 4.0k 0.9× 1.5k 1.1× 3.2k 2.6× 1.6k 1.7× 2.5k 2.9× 286 8.5k
José Luís Verdegay 2.2k 0.5× 2.7k 2.0× 4.4k 3.5× 1.8k 2.0× 1.6k 1.8× 164 7.8k

Countries citing papers authored by Fernando Gomide

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Gomide

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Gomide

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Gomide. A scholar is included among the top collaborators of Fernando Gomide 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 Fernando Gomide. Fernando Gomide 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.
Andonovski, Goran, et al.. (2025). Advancements in data-driven evolving fuzzy and neuro-fuzzy control: A comprehensive survey. Applied Soft Computing. 186. 114058–114058.
2.
Leite, Daniel, et al.. (2023). Interval incremental learning of interval data streams and application to vehicle tracking. Information Sciences. 630. 1–22. 12 indexed citations
3.
Pinheiro, Carlos & Fernando Gomide. (2016). Controle Neural de Sistemas Não Lineares por Resposta em Freqüência. 31–35.
4.
Leite, Daniel, Reinaldo M. Palhares, Víctor Costa da Silva Campos, & Fernando Gomide. (2014). Evolving Granular Fuzzy Model-Based Control of Nonlinear Dynamic Systems. IEEE Transactions on Fuzzy Systems. 23(4). 923–938. 76 indexed citations
5.
Leite, Daniel, Pyramo Costa, & Fernando Gomide. (2012). Evolving granular neural networks from fuzzy data streams. Neural Networks. 38. 1–16. 88 indexed citations
6.
Lemos, André, Walmir M. Caminhas, & Fernando Gomide. (2010). Multivariable Gaussian Evolving Fuzzy Modeling System. IEEE Transactions on Fuzzy Systems. 19(1). 91–104. 150 indexed citations
7.
Gomide, Fernando, et al.. (2009). Coevolutionary Genetic Fuzzy System to Assess Multiagent Bidding Strategies in Electricity Markets.. European Society for Fuzzy Logic and Technology Conference. 1114–1119. 1 indexed citations
8.
Ballini, Rosângela, et al.. (2007). Training Neurofuzzy Networks with Participatory Learning.. European Society for Fuzzy Logic and Technology Conference. 231–236. 4 indexed citations
9.
Gomide, Fernando, et al.. (2005). Hybrid Genetic Algorithms and Clustering. European Society for Fuzzy Logic and Technology Conference. 4(1). 1009–1016. 1 indexed citations
10.
Bassanezi, Rodney Carlos, et al.. (2004). Fuzzy modeling in symptomatic HIV virus infected population. Bulletin of Mathematical Biology. 66(6). 1597–1620. 60 indexed citations
11.
Gomide, Fernando, et al.. (2003). Genetic fuzzy systems to evolve coordination strategies in competitive distributed systems.. European Society for Fuzzy Logic and Technology Conference. 114–119. 1 indexed citations
12.
Carse, Brian, Tony Pipe, Antonio Skármeta, et al.. (2003). Current issues and future directions in evolutionary fuzzy systems research.. European Society for Fuzzy Logic and Technology Conference. 81–87. 1 indexed citations
13.
Oliveira, José Valente de & Fernando Gomide. (2001). Formal Methods for Fuzzy Modeling and Control. Fuzzy Sets and Systems. 121(1). 1–2. 4 indexed citations
14.
Gomide, Fernando, et al.. (1999). Design of fuzzy systems using neurofuzzy networks. IEEE Transactions on Neural Networks. 10(4). 815–827. 78 indexed citations
15.
Caminhas, Walmir M., Hermano Tavares, Fernando Gomide, & Witold Pedrycz. (1999). Fuzzy Set Based Neural Networks: Structure, Learning and Application. Journal of Advanced Computational Intelligence and Intelligent Informatics. 3(3). 151–157. 32 indexed citations
16.
Delgado, Myriam, Fernando J. Von Zuben, & Fernando Gomide. (1999). Modular and hierarchical evolutionary design of fuzzy systems. Genetic and Evolutionary Computation Conference. 112(4). 180–187. 3 indexed citations
17.
Gudwin, Ricardo & Fernando Gomide. (1997). An Approach to Computational Semiotics. 9 indexed citations
18.
Gudwin, Ricardo & Fernando Gomide. (1994). Context Adaptation in Fuzzy Processing. 5 indexed citations
19.
Freitas, Rubens Moreno de, et al.. (1994). Fuzzy distributed artificial intelligence systems. Acervo Digital da Universidade Estadual Paulista (Universidade Estadual Paulista). 462–467 vol.1. 23 indexed citations
20.
Gomide, Fernando, Anderson Rocha, & Pedro Albertos. (1992). Neurofuzzy Controllers. IFAC Proceedings Volumes. 25(25). 13–26. 12 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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