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- Title
Generalized backpropagation algorithm for training second‐order neural networks.
- Authors
Fan, Fenglei; Cong, Wenxiang; Wang, Ge
- Abstract
Abstract: The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to second‐order counterparts, in which the linear operation between inputs to a neuron and the associated weights is replaced with a nonlinear quadratic operation. A single second‐order neurons already have a strong nonlinear modeling ability, such as implementing basic fuzzy logic operations. In this paper, we develop a general backpropagation algorithm to train the network consisting of second‐order neurons. The numerical studies are performed to verify the generalized backpropagation algorithm.
- Subjects
BACK propagation; NEURONS; ARTIFICIAL neural networks; MACHINE learning; FUZZY logic
- Publication
International Journal for Numerical Methods in Biomedical Engineering, 2018, Vol 34, Issue 5, p1
- ISSN
2040-7939
- Publication type
Article
- DOI
10.1002/cnm.2956