Dimitrios Gerontitis

448 total citations
25 papers, 308 citations indexed

About

Dimitrios Gerontitis is a scholar working on Artificial Intelligence, Control and Systems Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Dimitrios Gerontitis has authored 25 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 15 papers in Control and Systems Engineering and 7 papers in Statistical and Nonlinear Physics. Recurrent topics in Dimitrios Gerontitis's work include Neural Networks and Applications (18 papers), Robotic Mechanisms and Dynamics (10 papers) and Model Reduction and Neural Networks (7 papers). Dimitrios Gerontitis is often cited by papers focused on Neural Networks and Applications (18 papers), Robotic Mechanisms and Dynamics (10 papers) and Model Reduction and Neural Networks (7 papers). Dimitrios Gerontitis collaborates with scholars based in Greece, China and Serbia. Dimitrios Gerontitis's co-authors include Predrag S. Stanimirović, Yang Shi, Ratikanta Behera, Marko D. Petković, Vasilios N. Katsikis, Shuai Li, Long Jin, Jipeng Qiang, Jian Li and Miroslav Ćirić and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing and Neural Networks.

In The Last Decade

Dimitrios Gerontitis

21 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dimitrios Gerontitis Greece 12 167 153 69 58 53 25 308
Hui‐Jie Sun China 10 178 1.1× 67 0.4× 77 1.1× 26 0.4× 49 0.9× 48 317
Weimu Ma China 8 238 1.4× 157 1.0× 34 0.5× 30 0.5× 71 1.3× 9 303
Can Tong China 7 265 1.6× 27 0.2× 67 1.0× 34 0.6× 36 0.7× 17 397
Xiqin He China 12 289 1.7× 59 0.4× 101 1.5× 24 0.4× 44 0.8× 38 401
Dian Sheng China 8 294 1.8× 83 0.5× 18 0.3× 45 0.8× 19 0.4× 11 369
Babak Hassibi United States 3 243 1.5× 107 0.7× 41 0.6× 16 0.3× 14 0.3× 6 374
Igor G. Vladimirov Australia 9 289 1.7× 112 0.7× 34 0.5× 31 0.5× 20 0.4× 51 468
Farouk Zouari Tunisia 15 345 2.1× 76 0.5× 81 1.2× 112 1.9× 17 0.3× 29 531

Countries citing papers authored by Dimitrios Gerontitis

Since Specialization
Citations

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

Fields of papers citing papers by Dimitrios Gerontitis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dimitrios Gerontitis

This figure shows the co-authorship network connecting the top 25 collaborators of Dimitrios Gerontitis. A scholar is included among the top collaborators of Dimitrios Gerontitis 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 Dimitrios Gerontitis. Dimitrios Gerontitis 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.
2.
Zhang, Hongyuan, et al.. (2025). New Recurrent Neural Network for Different-Level Sylvester Matrix Equation System: A Direct Discrete Architecture Perspective. IEEE Transactions on Emerging Topics in Computational Intelligence. 10(1). 583–593.
3.
Stanimirović, Predrag S., et al.. (2025). Generalized Matrix Inversion: A Machine Learning Approach.
4.
Shi, Yang, et al.. (2025). A New Double-Integration-Enhanced RNN Algorithm for Discrete Time-Variant Equation Systems With Robot Manipulator Applications. IEEE Transactions on Automation Science and Engineering. 22. 8856–8869. 2 indexed citations
5.
Gerontitis, Dimitrios, et al.. (2025). A New General Fast Neurodynamics (GFN) for Solving Complex Generalized Sylvester Equation With Power Systems Application. Mathematical Methods in the Applied Sciences. 48(11). 10678–10698. 4 indexed citations
6.
Gerontitis, Dimitrios, et al.. (2024). Solving the generalized Sylvester equation with a novel fast extended neurodynamics. Numerical Algebra Control and Optimization. 15(3). 619–644. 9 indexed citations
7.
Stanimirović, Predrag S., et al.. (2024). Application of Gradient Optimization Methods in Defining Neural Dynamics. Axioms. 13(1). 49–49. 2 indexed citations
8.
Gerontitis, Dimitrios, et al.. (2023). Improved zeroing neural models based on two novel activation functions with exponential behavior. Theoretical Computer Science. 986. 114328–114328. 3 indexed citations
9.
Shi, Yang, et al.. (2023). An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis. CAAI Transactions on Intelligence Technology. 8(3). 607–621. 13 indexed citations
10.
11.
Gerontitis, Dimitrios, et al.. (2023). A High Speed Convergent Formula for Time-Variant Generalized Sylvester Equation Solving. 1–5. 2 indexed citations
12.
Jin, Jie, et al.. (2023). Improved Recurrent Neural Networks for Text Classification and Dynamic Sylvester Equation Solving. Neural Processing Letters. 55(7). 8755–8784. 11 indexed citations
13.
Gerontitis, Dimitrios, et al.. (2023). A novel extended Li zeroing neural network for matrix inversion. Neural Computing and Applications. 35(19). 14129–14152. 14 indexed citations
14.
Gerontitis, Dimitrios, et al.. (2021). Solving the time-varying tensor square root equation by varying-parameters finite-time Zhang neural network. Neurocomputing. 445. 309–325. 19 indexed citations
15.
Gerontitis, Dimitrios, et al.. (2021). A family of varying-parameter finite-time zeroing neural networks for solving time-varying Sylvester equation and its application. Journal of Computational and Applied Mathematics. 403. 113826–113826. 30 indexed citations
16.
Shi, Yang, Long Jin, Shuai Li, et al.. (2020). Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application. IEEE Transactions on Neural Networks and Learning Systems. 33(2). 587–599. 64 indexed citations
17.
Gerontitis, Dimitrios, Lazaros Moysis, Predrag S. Stanimirović, Vasilios N. Katsikis, & Christos Volos. (2020). Varying‐parameter finite‐time zeroing neural network for solving linear algebraic systems. Electronics Letters. 56(16). 810–813. 15 indexed citations
18.
Stanimirović, Predrag S., Marko D. Petković, & Dimitrios Gerontitis. (2017). Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses and the Drazin Inverse. Neural Processing Letters. 48(1). 109–133. 35 indexed citations
19.
Stanimirović, Predrag S., et al.. (2017). Conditions for Existence, Representations, and Computation of Matrix Generalized Inverses. Complexity. 2017. 1–27. 25 indexed citations
20.
Stanimirović, Predrag S., et al.. (2017). ZNN models for computing matrix inverse based on hyperpower iterative methods. Filomat. 31(10). 2999–3014. 15 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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