IEEE Control Systems Magazine Template
Write in a clean editor, then format for IEEE Control Systems Magazine in one click — DocuGuru applies the official IEEE template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the IEEE Control Systems Magazine format
IEEE Control Systems Magazine is a peer-reviewed journal published by IEEE, covering Human auditory perception and evaluation, Diverse Scientific and Economic Studies, Educational Robotics and Engineering.
| 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 Control Systems Magazine, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Control Systems Magazine style. No sign-up. |
| Publishes research in | Human auditory perception and evaluation Diverse Scientific and Economic Studies Educational Robotics and Engineering Diverse Specialized Academic Research Earthquake and Disaster Impact Studies |
| ISSN | 0272-1708 |
| h-index | 61 |
| i10-index | 210 |
| Total citations | 18,672 |
| Top institutions publishing here | Massachusetts Institute of Technology |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in IEEE Control Systems Magazine per year
Citation impact of IEEE Control Systems Magazine by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in IEEE Control Systems Magazine
Techniques to detect and isolate failures in complex technological systems, such as sensor biases, actuator malfunctions, leaks, and equipment deterioration are surveyed. The methods are based on analytical redundancy afforded by a mathematical model of the system. The main components of such techniques are residual generation using the model, signature generation by statistical testing, and…
A multilayered neural network processor is used to control a given plant. Several learning architectures are proposed for training the neural controller to provide the appropriate inputs to the plant so that a desired response is obtained. A modified error-back propagation algorithm, based on propagation of the output error through the plant, is introduced. The…
It is shown that a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic…