The DOI matches its Crossref record.
Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective
Abstract
Artificial intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics and motor drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. This paper gives a comprehensive introduction and perspective of neural network applications in the intelligent control and estimation for power electronics and motor drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include nonlinear function generation, delayless filtering and waveform processing, feedback signal processing of vector drive, space vector PWM of two-level and multilevel inverters, adaptive flux vector estimation, and some of their combination for vector-controlled ac drive. Additional selected applications in the literature are included in the references. From the current trend of the technology, it appears that neural networks will find widespread applications in power electronics and motor drives in future
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Multilevel Converters: An Enabling Technology for High-Power Applications
Rodriguez, J., Franquelo, L. G., Kouro, S., Leon, J. I., Portillo, R. C., Prats, M. A. M., & Perez, M. A. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2030235 · Cited by 1,196
An Overview of Artificial Intelligence Applications for Power Electronics
Zhao, S., Blaabjerg, F., & Wang, H. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3024914 · Cited by 947
Global Energy Scenario and Impact of Power Electronics in 21st Century
Bose, B. K. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2203771 · Cited by 476
Self-citationPower Electronics and Motor Drives Recent Progress and Perspective
Bose, B. K. (2009) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2002726 · Cited by 431
Self-citationArtificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design
Guillod, T., Papamanolis, P., & W. Kolar, J. (2020) · IEEE Open Journal of Power Electronics · doi:10.1109/ojpel.2020.3012777 · Cited by 264
Metadata
- Authors
- Bose, B. K.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2007-02
- Volume
- 54
- Issue
- 1
- Pages
- 14-33
- ISSN
- 0278-0046 (ISSN-L)
- OpenAlex
- W2167175022
- Cited by
- 520
- References
- 0
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective. https://app.cochat.ai/papers/10.1109/tie.2006.888683
- The paper
- Bose, B. K. (2007). Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2006.888683
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.