Multi-back-propagation Algorithm for Signal Neural Network Decomposition

In this paper, a novel back-propagation error technique is presented. This neural network structure allows for two fundamental basic modes: (1) To decompose the neurones by transforming their variables, weights, and scalar functions into vectors. This conveys for the decomposition of the transfer fu...

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Veröffentlicht in:	Neural processing letters Jg. 56; H. 2; S. 100
Hauptverfasser:	Salgado, Paulo, Perdicoúlis, T.-P. Azevedo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 12.03.2024 Springer Nature B.V
Schlagworte:	Algorithms Approximation Artificial Intelligence Back propagation Back propagation networks Brain research Channels Classification Complex Systems Computational Intelligence Computer Science Data analysis Decomposition Mathematical functions Neural networks Orthogonal functions Orthogonality Pattern analysis Pattern recognition Propagation Transfer functions Network decomposition Back-propagation Orthogonal decomposition Fourier series Multivariable neurone Network splitting Neural network training
ISSN:	1573-773X, 1370-4621, 1573-773X
Online-Zugang:	Volltext
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