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- W4366126586 abstract "The paper aims to introduce and illustrate a novel kind of nonlinearity to be used as an activation function for the neurons of a Neural Network (NN). It is shown how the proposed function can be regarded as a generalization of the standard sigmoid. For this reason, it is referred to as quasi-sigmoid. The features of a backpropagation algorithm based on quasi-sigmoids are illustrated and commented. The properties of quasi-sigmoidal networks are compared to basic sigmoidal models on benchmark test cases as well as on sample function approximation problems in electromagnetics. The performance of quasi-sigmoidal networks as function approximators are shown to be generally superior to sigmoidal one by reason of the flexibility introduced and/or in terms of degrees of freedom of the model. As a by-product, the proposed activation function allows to carry out data-driven detections of nonlinearity in the data." @default.
- W4366126586 created "2023-04-19" @default.
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- W4366126586 date "2023-01-01" @default.
- W4366126586 modified "2023-10-16" @default.
- W4366126586 title "On the Use of Quasi-Sigmoids in Function Approximation Problems with Neural Networks" @default.
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- W4366126586 doi "https://doi.org/10.1007/978-3-031-25759-9_11" @default.
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