Pingan He

468 total citations
10 papers, 281 citations indexed

About

Pingan He is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Pingan He has authored 10 papers receiving a total of 281 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Control and Systems Engineering, 6 papers in Computational Theory and Mathematics and 4 papers in Artificial Intelligence. Recurrent topics in Pingan He's work include Adaptive Dynamic Programming Control (6 papers), Adaptive Control of Nonlinear Systems (6 papers) and Machine Learning and ELM (4 papers). Pingan He is often cited by papers focused on Adaptive Dynamic Programming Control (6 papers), Adaptive Control of Nonlinear Systems (6 papers) and Machine Learning and ELM (4 papers). Pingan He collaborates with scholars based in United States, China and India. Pingan He's co-authors include S. Jagannathan, Wenzhi Gao, Brian Kaul, J. A. Drallmeier, Yu Yang, Xiaoling Peng and Qiaolin He and has published in prestigious journals such as Automatica, Neural Networks and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).

In The Last Decade

Pingan He

9 papers receiving 271 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pingan He United States	6	201	186	109	44	23	10	281
Ahmed Maidi Algeria	10	247 1.2×	89 0.5×	19 0.2×	19 0.4×	10 0.4×	35	330
N.F. Al-Muthairi Kuwait	7	533 2.7×	123 0.7×	27 0.2×	49 1.1×	156 6.8×	14	564
Elvio A. Pilotta Argentina	9	54 0.3×	131 0.7×	25 0.2×	53 1.2×	3 0.1×	12	291
Tianjiao An China	9	190 0.9×	167 0.9×	99 0.9×	28 0.6×	48 2.1×	65	310
Anders Helmersson Sweden	10	315 1.6×	65 0.3×	27 0.2×	27 0.6×	19 0.8×	31	369
Sung-Hoe Huh South Korea	6	220 1.1×	51 0.3×	100 0.9×	92 2.1×	31 1.3×	11	308
Fanghua Tang China	6	249 1.2×	109 0.6×	52 0.5×	61 1.4×	143 6.2×	8	363
Roman Prokop Czechia	10	305 1.5×	43 0.2×	12 0.1×	38 0.9×	17 0.7×	77	359
Liangdong Guo China	11	278 1.4×	63 0.3×	58 0.5×	25 0.6×	125 5.4×	35	348
Russell Enns United States	6	155 0.8×	200 1.1×	137 1.3×	59 1.3×	13 0.6×	14	323

Countries citing papers authored by Pingan He

Since Specialization

Citations

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

Fields of papers citing papers by Pingan He

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pingan He

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Yang, Yu, Pingan He, Xiaoling Peng, & Qiaolin He. (2025). A novel number-theoretic sampling method for neural network solutions of partial differential equations. Neural Networks. 192. 107945–107945.

2.

He, Pingan, et al.. (2009). Unknown input estimation for a class of nonlinear systems and its application to automotive engine controls. 1195–1200. 5 indexed citations

3.

Jagannathan, S. & Pingan He. (2008). Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form. IEEE Transactions on Neural Networks. 19(12). 2073–2087. 44 indexed citations

4.

He, Pingan & S. Jagannathan. (2007). Reinforcement Learning Neural-Network-Based Controller for Nonlinear Discrete-Time Systems With Input Constraints. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 37(2). 425–436. 190 indexed citations

5.

He, Pingan, Wenzhi Gao, & S. Jagannathan. (2006). Neuro control of nonlinear discrete time systems with deadzone and input constraints. 61. 2836–2841. 5 indexed citations

6.

He, Pingan, et al.. (2006). Neural network-based output feedback controller for lean operation of spark ignition engines. 8 pp.–8 pp.. 10 indexed citations

7.

He, Pingan, et al.. (2006). Neural Network-based Control of Nonlinear Discrete-Time Svstems in Non-Strict Form. 2. 2580–2585. 3 indexed citations

8.

He, Pingan & S. Jagannathan. (2005). Neuro-controller for reducing cyclic variation in lean combustion spark ignition engines. Automatica. 41(7). 1133–1142. 17 indexed citations

9.

He, Pingan, et al.. (2005). Neural network controller for manipulation of micro-scale objects. i. 55–60. 2 indexed citations

10.

He, Pingan & S. Jagannathan. (2004). Reinforcement learning-based output feedback control of nonlinear systems with input constraints. 2563–2568 vol.3. 5 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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