Hai-Jun Rong

2.1k total citations
49 papers, 1.6k citations indexed

About

Hai-Jun Rong is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hai-Jun Rong has authored 49 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 12 papers in Control and Systems Engineering and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hai-Jun Rong's work include Neural Networks and Applications (26 papers), Machine Learning and ELM (19 papers) and Fuzzy Logic and Control Systems (15 papers). Hai-Jun Rong is often cited by papers focused on Neural Networks and Applications (26 papers), Machine Learning and ELM (19 papers) and Fuzzy Logic and Control Systems (15 papers). Hai-Jun Rong collaborates with scholars based in China, Singapore and Macao. Hai-Jun Rong's co-authors include Guang-Bin Huang, N. Sundararajan, P. Saratchandran, Yew-Soon Ong, Zexuan Zhu, Ah‐Hwee Tan, Guangshe Zhao, Plamen Angelov, Badong Chen and Nanying Liang and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Geoscience and Remote Sensing and IEEE Access.

In The Last Decade

Hai-Jun Rong

45 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hai-Jun Rong China 16 1.3k 330 260 249 103 49 1.6k
Zuwairie Ibrahim Malaysia 20 785 0.6× 262 0.8× 155 0.6× 297 1.2× 91 0.9× 213 1.7k
Radu‐Codruţ David Romania 18 653 0.5× 692 2.1× 200 0.8× 236 0.9× 100 1.0× 42 1.5k
Shuang Feng China 13 903 0.7× 370 1.1× 141 0.5× 403 1.6× 80 0.8× 31 1.3k
Mojtaba Ahmadieh Khanesar Iran 23 782 0.6× 727 2.2× 346 1.3× 120 0.5× 198 1.9× 71 1.6k
Antonio Rodríguez-Díaz Mexico 15 792 0.6× 307 0.9× 82 0.3× 102 0.4× 75 0.7× 46 1.5k
Claudiu Pozna Romania 15 364 0.3× 400 1.2× 150 0.6× 210 0.8× 110 1.1× 77 1.0k
Yasin Yılmaz United States 19 645 0.5× 443 1.3× 402 1.5× 176 0.7× 719 7.0× 90 1.4k
Yiannis S. Boutalis Greece 25 855 0.7× 567 1.7× 335 1.3× 837 3.4× 127 1.2× 131 2.4k
Fei Han China 21 880 0.7× 171 0.5× 185 0.7× 305 1.2× 55 0.5× 69 1.4k
Qing Song Singapore 18 392 0.3× 147 0.4× 134 0.5× 354 1.4× 50 0.5× 74 1.0k

Countries citing papers authored by Hai-Jun Rong

Since Specialization
Citations

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

Fields of papers citing papers by Hai-Jun Rong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai-Jun Rong. 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 Hai-Jun Rong. The network helps show where Hai-Jun Rong may publish in the future.

Co-authorship network of co-authors of Hai-Jun Rong

This figure shows the co-authorship network connecting the top 25 collaborators of Hai-Jun Rong. A scholar is included among the top collaborators of Hai-Jun Rong 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 Hai-Jun Rong. Hai-Jun Rong 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.
Yang, Zhao-Xu & Hai-Jun Rong. (2024). Evolving kernel-based fuzzy system with nonlinear consequences. Applied Soft Computing. 167. 112384–112384.
2.
Rong, Hai-Jun, et al.. (2023). Multilayer Stacked Evolving Fuzzy System Combined With Compressed Representation Learning. IEEE Transactions on Fuzzy Systems. 32(4). 2223–2234. 3 indexed citations
3.
Huang, Hui, Hai-Jun Rong, Zhao-Xu Yang, & Chi‐Man Vong. (2022). Recursive least mean dual p-power solution to the generalization of evolving fuzzy system under multiple noises. Information Sciences. 609. 228–247. 7 indexed citations
4.
Yang, Zhao-Xu, Hai-Jun Rong, Plamen Angelov, & Zhi-Xin Yang. (2021). Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises. IEEE Transactions on Fuzzy Systems. 30(7). 2649–2664. 21 indexed citations
5.
Yang, Zhao-Xu, et al.. (2020). Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. IEEE Transactions on Industrial Electronics. 68(5). 4508–4518. 13 indexed citations
6.
Rong, Hai-Jun, et al.. (2019). A Novel Prognostic Approach for RUL Estimation With Evolving Joint Prediction of Continuous and Discrete States. IEEE Transactions on Industrial Informatics. 15(9). 5089–5098. 8 indexed citations
7.
Gu, Xiaowei, Plamen Angelov, & Hai-Jun Rong. (2019). Local optimality of self-organising neuro-fuzzy inference systems. Information Sciences. 503. 351–380. 19 indexed citations
8.
Rong, Hai-Jun, et al.. (2018). Stability of Evolving Fuzzy Systems Based on Data Clouds. IEEE Transactions on Fuzzy Systems. 26(5). 2774–2784. 23 indexed citations
9.
Yang, Jing, Yi Xu, Hai-Jun Rong, Shaoyi Du, & Badong Chen. (2018). Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression. IEEE Access. 6. 16022–16034. 8 indexed citations
10.
Rong, Hai-Jun, Plamen Angelov, Badong Chen, & Pak Kin Wong. (2017). Correntropy-Based Evolving Fuzzy Neural System. IEEE Transactions on Fuzzy Systems. 26(3). 1324–1338. 57 indexed citations
11.
Yang, Jing, Ye Feng, Hai-Jun Rong, & Badong Chen. (2017). Recursive least meanp-power Extreme Learning Machine. Neural Networks. 91. 22–33. 11 indexed citations
12.
Yang, Zhao-Xu, Hai-Jun Rong, Guangshe Zhao, & Jing Yang. (2017). Self-evolving kernel recursive least squares algorithm for control and prediction. 1–8. 1 indexed citations
13.
Wang, Fei, et al.. (2016). Kernel adaptive filtering under generalized Maximum Correntropy Criterion. 1738–1745. 37 indexed citations
14.
Yang, Zhao-Xu, et al.. (2016). Robust kernel-based model reference adaptive control for unstable aircraft. Advances in Mechanical Engineering. 8(11). 4 indexed citations
15.
Rong, Hai-Jun, Zhao-Xu Yang, Pak Kin Wong, & Chi‐Man Vong. (2016). Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles. Journal of Dynamic Systems Measurement and Control. 139(4). 7 indexed citations
16.
Han, Chongzhao, et al.. (2014). New Design of Small Cardinality Model Set for Tracking Controllable-Structure Semiballistic Reentry Vehicle. Journal of Aerospace Engineering. 28(4). 1 indexed citations
17.
Rong, Hai-Jun, et al.. (2013). FUZZY EXTREME LEARNING MACHINE FOR A CLASS OF FUZZY INFERENCE SYSTEMS. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 21(supp02). 51–61. 9 indexed citations
18.
Rong, Hai-Jun, et al.. (2013). Adaptive fuzzy control of aircraft wing-rock motion. Applied Soft Computing. 14. 181–193. 32 indexed citations
19.
Rong, Hai-Jun, Guang-Bin Huang, N. Sundararajan, & P. Saratchandran. (2009). Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 39(4). 1067–1072. 277 indexed citations
20.
Huang, Guang-Bin, Nanying Liang, Hai-Jun Rong, P. Saratchandran, & N. Sundararajan. (2005). On-Line Sequential Extreme Learning Machine. Computational intelligence. 13. 232–237. 147 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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