IEEE Transactions on Neural Networks Template
Write in a clean editor, then format for IEEE Transactions on Neural Networks 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 Transactions on Neural Networks format
IEEE Transactions on Neural Networks is a peer-reviewed journal published by IEEE, covering Neural Networks and Applications, Face and Expression Recognition, Blind Source Separation Techniques.
| 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 Neural Networks, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Neural Networks style. No sign-up. |
| Publishes research in | Neural Networks and Applications Face and Expression Recognition Blind Source Separation Techniques Advanced Memory and Neural Computing Fuzzy Logic and Control Systems |
| ISSN | 1045-9227 |
| h-index | 266 |
| i10-index | 2,841 |
| Total citations | 469,139 |
| Top institutions publishing here | Nanyang Technological University |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in IEEE Transactions on Neural Networks per year
Citation impact of IEEE Transactions on Neural Networks 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 Transactions on Neural Networks
An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing…
Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms…
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel…