The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
A comprehensive review for industrial applicability of artificial neural networks
Abstract
This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.
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
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Identification and control of dynamical systems using neural networks
Narendra, K. S., & Parthasarathy, K. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80202 · Cited by 8,014
Training feedforward networks with the Marquardt algorithm
Hagan, M. T., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,701
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,218
Cited by
Artificial intelligence for fault diagnosis of rotating machinery: A review
Liu, R., Yang, B., Zio, E., & Chen, X. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.02.016 · Cited by 2,188
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
Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics
Javed, K., Gouriveau, R., Zerhouni, N., & Nectoux, P. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2327917 · Cited by 351
Neuromorphic Hardware System for Visual Pattern Recognition With Memristor Array and CMOS Neuron
Chu, M., Kim, B., Park, S., Hwang, H., Jeon, M., Lee, B. H., & Lee, B.-G. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2356439 · Cited by 296
Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology
Argatov, I. (2019) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2019.00030 · Cited by 97
Metadata
- Authors
- Meireles, M. R. G., Almeida, P. E. M., & Simoes, M. G.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2003-06
- Volume
- 50
- Issue
- 3
- Pages
- 585-601
- ISSN
- 0278-0046 (ISSN-L)
- OpenAlex
- W2170423728
- Cited by
- 482
- References
- 69
- FWCI
- 10.60
- 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: A comprehensive review for industrial applicability of artificial neural networks. https://app.cochat.ai/papers/10.1109/tie.2003.812470
- The paper
- Meireles, M. R. G., Almeida, P. E. M., & Simoes, M. G. (2003). A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2003.812470
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.
