IEEE Transactions on Cybernetics

5.5k papers and 255.3k indexed citations
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About

The 5.5k papers published in IEEE Transactions on Cybernetics in the last decades have received a total of 255.3k indexed citations. Papers published in IEEE Transactions on Cybernetics usually cover Control and Systems Engineering (1.9k papers), Artificial Intelligence (1.8k papers) and Computer Networks and Communications (1.7k papers) specifically the topics of Distributed Control Multi-Agent Systems (1.0k papers), Neural Networks Stability and Synchronization (877 papers) and Adaptive Control of Nonlinear Systems (733 papers). The most active scholars publishing in IEEE Transactions on Cybernetics are C. L. Philip Chen, Qing‐Long Han, Peng Shi, Shaocheng Tong, Zidong Wang, Guang‐Hong Yang, Yaochu Jin, Xuelong Li, Hongyi Li and Tingwen Huang.

In The Last Decade

IEEE Transactions on Cybernetics

5.3k papers receiving 246.6k citations

Fields of papers published in IEEE Transactions on Cybernetics

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in IEEE Transactions on Cybernetics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in IEEE Transactions on Cybernetics.

Countries where authors publish in IEEE Transactions on Cybernetics

Since Specialization
Citations

This map shows the geographic impact of research published in IEEE Transactions on Cybernetics. 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 papers published in IEEE Transactions on Cybernetics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites IEEE Transactions on Cybernetics more than expected).

Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints... 2013 2026 2017 2021 945
  1. Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2015)
  2. Enhanced Computer Vision With Microsoft Kinect Sensor: A Review (2013)
  3. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2020)
  4. Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2013)
  5. Granular Computing: Perspectives and Challenges (2013)
  6. Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form (2014)
  7. A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2012)
  8. Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone (2015)
  9. Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2018)
  10. A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2016)
  11. Granular Computing Approach to Two-Way Learning Based on Formal Concept Analysis in Fuzzy Datasets (2014)
  12. Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation (2013)
  13. Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints (2015)
  14. GreenSea: Visual Soccer Analysis Using Broad Learning System (2020)
  15. Multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks (2017)

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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