A new method for building single feedforward neural network models for multivariate static regression problems: a combined weight initialization and constructive algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s12065-022-00813-z.pdf
Reference50 articles.
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3. Han F, Zhao MR, Zhang JM, Ling QH (2017) An improved incremental constructive single-hidden-layer feedforward networks for extreme learning machine based on particle swarm optimization. Neurocomputing 228:133–142. https://doi.org/10.1016/j.neucom.2016.09.092
4. Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278–287. https://doi.org/10.1016/j.neucom.2017.08.040
5. Dolezel P, Skrabanek P, Gago L (2016) Weight initialization possibilities for feedforward neural network with linear saturated activation function. IFAC-PapersOnLine 49–25:049–054. https://doi.org/10.1016/j.ifacol.2016.12.009
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