Radial basis function neural network training using variable projection and fuzzy means
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-10274-3.pdf
Reference50 articles.
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3. Liu T, Chen S, Liang S et al (2020) Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes. J Process Control. https://doi.org/10.1016/j.jprocont.2020.07.009
4. Karamichailidou D, Koletsios S, Alexandridis A (2022) An RBF online learning scheme for non-stationary environments based on fuzzy means and Givens rotations. Neurocomputing 501:370–386. https://doi.org/10.1016/j.neucom.2022.06.016
5. Alexandridis A, Chondrodima E, Giannopoulos N, Sarimveis H (2017) A fast and efficient method for training categorical radial basis function networks. IEEE Trans Neural Netw Learn Syst 28:2831–2836
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