Supervised and unsupervised learning in radial basis function classifiers
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date1994/01/01
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Indian UGC (journal)
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Citations39
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I. Tarassenko
Cite
Tarassenko, I. “Supervised and Unsupervised Learning in Radial Basis Function Classifiers”. IEE Proceedings - Vision, Image, and Signal Processing, vol. 141, no. 4, 1994, p. 210, https://doi.org/10.1049/ip-vis:19941324.
Tarassenko, I. (1994). Supervised and unsupervised learning in radial basis function classifiers. IEE Proceedings - Vision, Image, and Signal Processing, 141(4), 210. https://doi.org/10.1049/ip-vis:19941324
Tarassenko I. Supervised and unsupervised learning in radial basis function classifiers. IEE Proceedings - Vision, Image, and Signal Processing. 1994;141(4):210.
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture | Energies |
| 3 | 2020 |
| Generalized three phase robust load-flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks | Journal of Cleaner Production |
| 32 | 2018 |
| Unbalanced harmonic power sharing and voltage compensation of microgrids using radial basis function neural network‐based harmonic power‐flow calculations for distributed and decentralised control structures | IET Generation, Transmission & Distribution |
| 66 | 2018 |
| Nonlinear Load Sharing and Voltage Compensation of Microgrids Based on Harmonic Power-Flow Calculations Using Radial Basis Function Neural Networks | IEEE Systems Journal |
| 1 | 2018 |
Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets | International Journal of Operations Research and Information Systems | 2018 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics 15
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A global optimization technique for statistical classifier design and was published in 1996. The most recent citation comes from a 2020 study titled Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture. This article reached its peak citation in 2014, with 7 citations. It has been cited in 31 different journals, 12% of which are open access. Among related journals, the Open Journal of Applied Sciences cited this research the most, with 7 citations. The chart below illustrates the annual citation trends for this article.