G.G. Parma

431 total citations
14 papers, 343 citations indexed

About

G.G. Parma is a scholar working on Control and Systems Engineering, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, G.G. Parma has authored 14 papers receiving a total of 343 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Control and Systems Engineering, 10 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in G.G. Parma's work include Neural Networks and Applications (9 papers), Advanced Algorithms and Applications (3 papers) and Fault Detection and Control Systems (3 papers). G.G. Parma is often cited by papers focused on Neural Networks and Applications (9 papers), Advanced Algorithms and Applications (3 papers) and Fault Detection and Control Systems (3 papers). G.G. Parma collaborates with scholars based in Brazil, Canada and United Kingdom. G.G. Parma's co-authors include Venkata Dinavahi, Antônio P. Braga, B.R. Menezes, Marcelo Azevedo Costa, Ademir Nied, Seleme Isaac Seleme, Arthur P. S. Braga and Walmir M. Caminhas and has published in prestigious journals such as IEEE Transactions on Power Delivery, Neurocomputing and Electronics Letters.

In The Last Decade

G.G. Parma

14 papers receiving 315 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
G.G. Parma Brazil 7 298 188 96 44 18 14 343
Muhammad Shoaib Almas Sweden 15 523 1.8× 533 2.8× 31 0.3× 12 0.3× 12 0.7× 43 620
Elı́as Todorovich Argentina 9 72 0.2× 161 0.9× 32 0.3× 34 0.8× 64 3.6× 30 235
Mahendra Prasad India 8 104 0.3× 174 0.9× 154 1.6× 13 0.3× 13 0.7× 10 376
G. Cauley United States 8 319 1.1× 577 3.1× 40 0.4× 24 0.5× 5 0.3× 11 662
Hongxia Rao China 14 219 0.7× 120 0.6× 101 1.1× 21 0.5× 3 0.2× 43 480
F. Eugenio Villaseca United States 9 193 0.6× 331 1.8× 61 0.6× 37 0.8× 1 0.1× 20 436
Philip Top United States 9 208 0.7× 221 1.2× 25 0.3× 10 0.2× 14 0.8× 23 308
Yuh-Shyang Wang United States 10 217 0.7× 31 0.2× 31 0.3× 48 1.1× 15 0.8× 15 278
Yanmin Wu China 4 290 1.0× 48 0.3× 73 0.8× 75 1.7× 3 0.2× 12 391
Chang Zhao China 7 246 0.8× 124 0.7× 24 0.3× 19 0.4× 4 0.2× 14 343

Countries citing papers authored by G.G. Parma

Since Specialization
Citations

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

Fields of papers citing papers by G.G. Parma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G.G. Parma

This figure shows the co-authorship network connecting the top 25 collaborators of G.G. Parma. A scholar is included among the top collaborators of G.G. Parma 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 G.G. Parma. G.G. Parma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
2.
Menezes, B.R., et al.. (2016). A New Neurofuzzy Controller Based on NFN Networks. 1–7. 1 indexed citations
3.
Nied, Ademir, Seleme Isaac Seleme, G.G. Parma, & B.R. Menezes. (2007). On-line neural training algorithm with sliding mode control and adaptive learning rate. Neurocomputing. 70(16-18). 2687–2691. 24 indexed citations
4.
Parma, G.G. & Venkata Dinavahi. (2007). Real-Time Digital Hardware Simulation of Power Electronics and Drives. IEEE Transactions on Power Delivery. 22(2). 1235–1246. 175 indexed citations
5.
Parma, G.G. & Venkata Dinavahi. (2007). Real-Time Digital Hardware Simulation of Power Electronics and Drives. IEEE Power Engineering Society General Meeting. 1–1. 9 indexed citations
6.
Nied, Ademir, et al.. (2006). On-line adaptive neural training algorithm for an induction motor flux observer. 1. 110–115. 5 indexed citations
7.
Nied, Ademir, et al.. (2004). On-line training algorithms for an induction motor stator flux neural observer. 129–134. 2 indexed citations
8.
Parma, G.G., B.R. Menezes, & Arthur P. S. Braga. (2003). Sliding mode backpropagation: control theory applied to neural network learning. 3. 1774–1778. 2 indexed citations
9.
Costa, Marcelo Azevedo, et al.. (2003). Training neural networks with a multi-objective sliding mode control algorithm. Neurocomputing. 51. 467–473. 19 indexed citations
10.
Parma, G.G., B.R. Menezes, Antônio P. Braga, & Marcelo Azevedo Costa. (2003). Sliding mode neural network control of an induction motor drive. International Journal of Adaptive Control and Signal Processing. 17(6). 501–508. 22 indexed citations
11.
Costa, Marcelo Azevedo, et al.. (2003). Control of generalization with a bi-objective sliding mode control algorithm. 4. 38–43. 1 indexed citations
12.
Parma, G.G., B.R. Menezes, & Antônio P. Braga. (2002). Improving backpropagation with sliding mode control. 8–13. 4 indexed citations
13.
Parma, G.G., et al.. (1999). Neural Networks Learning With Sliding Mode Control: The Sliding Mode Backpropagation Algorithm. International Journal of Neural Systems. 9(3). 187–193. 16 indexed citations
14.
Parma, G.G., B.R. Menezes, & Antônio P. Braga. (1998). Sliding mode algorithm for training multilayerartificial neural networks. Electronics Letters. 34(1). 97–98. 62 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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