T. Radhika

824 total citations · 1 hit paper
24 papers, 555 citations indexed

About

T. Radhika is a scholar working on Computer Networks and Communications, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, T. Radhika has authored 24 papers receiving a total of 555 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Networks and Communications, 10 papers in Control and Systems Engineering and 7 papers in Artificial Intelligence. Recurrent topics in T. Radhika's work include Neural Networks Stability and Synchronization (22 papers), Advanced Memory and Neural Computing (7 papers) and Stability and Control of Uncertain Systems (7 papers). T. Radhika is often cited by papers focused on Neural Networks Stability and Synchronization (22 papers), Advanced Memory and Neural Computing (7 papers) and Stability and Control of Uncertain Systems (7 papers). T. Radhika collaborates with scholars based in India, China and Germany. T. Radhika's co-authors include A. Chandrasekar, Quanxin Zhu, G. Nagamani, V. Vijayakumar, Yang Cao, Muhammad Shamrooz Aslam, R. Rakkiyappan, P. Balasubramaniam, S. M. Ramasamy and Young Hoon Joo and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, International Journal of Electrical Power & Energy Systems and Nonlinear Dynamics.

In The Last Decade

T. Radhika

19 papers receiving 550 citations

Hit Papers

1 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Analysis of Markovian Jump Stochastic Cohen–Grossberg BAM Neural Networks with Time Delays for Exponential Input-to-State Stability

2023 105 citations T. Radhika, A. Chandrasekar et al. Neural Processing Letters profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T. Radhika India	12	204	203	141	110	106	24	555
Gonzalo Joya Spain	13	329 1.6×	190 0.9×	184 1.3×	62 0.6×	312 2.9×	59	791
Deqiang Ouyang China	15	237 1.2×	404 2.0×	155 1.1×	274 2.5×	103 1.0×	36	860
Chunyan Han China	12	189 0.9×	184 0.9×	338 2.4×	146 1.3×	79 0.7×	111	661
Yixian Yang China	13	255 1.3×	162 0.8×	56 0.4×	126 1.1×	59 0.6×	95	549
Xiao Yu China	13	210 1.0×	101 0.5×	39 0.3×	127 1.2×	79 0.7×	35	562
Jochen Gorski Germany	6	136 0.7×	102 0.5×	75 0.5×	111 1.0×	141 1.3×	11	596
Jorge M. Cruz‐Duarte Mexico	14	245 1.2×	57 0.3×	70 0.5×	73 0.7×	86 0.8×	73	672
Fernando Gama United States	13	531 2.6×	220 1.1×	96 0.7×	237 2.2×	193 1.8×	44	927
Abdelkrim Boukabou Algeria	18	179 0.9×	218 1.1×	212 1.5×	249 2.3×	190 1.8×	68	899

Countries citing papers authored by T. Radhika

Since Specialization

Citations

This map shows the geographic impact of T. Radhika'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 T. Radhika with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Radhika more than expected).

Fields of papers citing papers by T. Radhika

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. Radhika

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

All Works

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20 of 20 papers shown

Chandrasekar, A., et al.. (2025). Resilient memory sampled-data controller for synchronization of semi-Markovian jump competitive neural networks with mixed delays. International Journal of Electrical Power & Energy Systems. 171. 110982–110982.

Radhika, T., et al.. (2025). Robust dissipative sliding mode control synchronization of memristive inertial competitive neural networks with time-varying delay. The European Physical Journal Special Topics. 6 indexed citations

Radhika, T., et al.. (2025). Robust Sampled‐Data Synchronization of Memristor Inertial Competitive Neural Networks With Two Delay Components. Mathematical Methods in the Applied Sciences. 48(6). 6764–6778.

Radhika, T., A. Chandrasekar, & V. Vijayakumar. (2024). Finite-time H∞ synchronization of semi-Markov jump neural networks with two delay components with stochastic sampled-data control. Bulletin des Sciences Mathématiques. 195. 103482–103482. 11 indexed citations

Cao, Yang, et al.. (2024). Exponential State Estimation for Delayed Competitive Neural Network Via Stochastic Sampled-Data Control with Markov Jump Parameters Under Actuator Failure. Journal of Artificial Intelligence and Soft Computing Research. 14(4). 373–385. 36 indexed citations

Aslam, Muhammad Shamrooz, T. Radhika, A. Chandrasekar, & Quanxin Zhu. (2024). Improved Event-Triggered-Based Output Tracking for a Class of Delayed Networked T–S Fuzzy Systems. International Journal of Fuzzy Systems. 26(4). 1247–1260. 40 indexed citations

Radhika, T., et al.. (2024). Robust dissipativity analysis for stochastic Markov jump competitive neural networks with mixed delays. Journal of Applied Mathematics and Computing. 71(1). 801–828.

Radhika, T., et al.. (2024). Synchronization of Markov jump neural networks with two delay components via Affine transformed sampled-data control with actuator saturation. The European Physical Journal Plus. 139(10).

Cao, Yang, A. Chandrasekar, T. Radhika, & V. Vijayakumar. (2023). Input-to-state stability of stochastic Markovian jump genetic regulatory networks. Mathematics and Computers in Simulation. 222. 174–187. 79 indexed citations

10.

Radhika, T., A. Chandrasekar, V. Vijayakumar, & Quanxin Zhu. (2023). Analysis of Markovian Jump Stochastic Cohen–Grossberg BAM Neural Networks with Time Delays for Exponential Input-to-State Stability. Neural Processing Letters. 55(8). 11055–11072. 105 indexed citations breakdown →

11.

Nagamani, G., Young Hoon Joo, & T. Radhika. (2018). Delay-dependent dissipativity criteria for Markovian jump neural networks with random delays and incomplete transition probabilities. Nonlinear Dynamics. 91(4). 2503–2522. 13 indexed citations

12.

Radhika, T., G. Nagamani, Quanxin Zhu, S. M. Ramasamy, & R. Saravanakumar. (2017). Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays. Neural Computing and Applications. 30(11). 3565–3579. 11 indexed citations

13.

Nagamani, G., T. Radhika, & Quanxin Zhu. (2016). An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components. IEEE Transactions on Neural Networks and Learning Systems. 28(12). 3018–3031. 26 indexed citations

14.

Ramasamy, S. M., G. Nagamani, & T. Radhika. (2016). Further Results on Dissipativity Criterion for Markovian Jump Discrete-Time Neural Networks with Two Delay Components Via Discrete Wirtinger Inequality Approach. Neural Processing Letters. 45(3). 939–965. 8 indexed citations

15.

Nagamani, G., T. Radhika, & P. Gopalakrishnan. (2016). Dissipativity and passivity analysis of Markovian jump impulsive neural networks with time delays. International Journal of Computer Mathematics. 94(7). 1479–1500. 10 indexed citations

16.

Radhika, T. & G. Nagamani. (2016). Dissipativity analysis of stochastic memristor-based recurrent neural networks with discrete and distributed time-varying delays. Network Computation in Neural Systems. 27(4). 237–267. 4 indexed citations

17.

Nagamani, G. & T. Radhika. (2016). A quadratic convex combination approach on robust dissipativity and passivity analysis for Takagi–Sugeno fuzzy Cohen–Grossberg neural networks with time‐varying delays. Mathematical Methods in the Applied Sciences. 39(13). 3880–3896. 9 indexed citations

18.

Nagamani, G., T. Radhika, & P. Balasubramaniam. (2015). A delay decomposition approach for robust dissipativity and passivity analysis of neutral‐type neural networks with leakage time‐varying delay. Complexity. 21(5). 248–264. 18 indexed citations

19.

Nagamani, G. & T. Radhika. (2015). Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach. Nonlinear Dynamics. 82(3). 1325–1341. 16 indexed citations

20.

Rakkiyappan, R., Quanxin Zhu, & T. Radhika. (2013). Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach. Nonlinear Dynamics. 73(3). 1367–1383. 25 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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