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IEEE Transactions on Cybernetics is a peer-reviewed journal published by IEEE, covering Distributed Control Multi-Agent Systems, Neural Networks Stability and Synchronization, Adaptive Control of Nonlinear Systems. Over its lifetime it has published 7,224 papers, which have received 386,671 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Cybernetics, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Cybernetics style. No sign-up. |
| Abbreviation (ISO-4) | TCYB |
| Publishes research in | Distributed Control Multi-Agent Systems Neural Networks Stability and Synchronization Adaptive Control of Nonlinear Systems Stability and Control of Uncertain Systems Adaptive Dynamic Programming Control |
| ISSN | 2168-2267 |
| Citation impact (2-yr) | 7.13 |
| h-index | 244 |
| i10-index | 5,025 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding-box regression and nonmaximum suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider…
With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based…
Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It…
This paper studies the tracking control problem for an uncertain n -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties…
Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging…
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