The Construction and Approximation of ReLU Neural Network Operators

The Construction and Approximation of ReLU Neural Network Operators

In the present paper, we construct a new type of two-hidden-layer feedforward neural network operators with ReLU activation function. We estimate the rate of approximation by the new operators by using the modulus of continuity of the target function. Furthermore, we analyze features such as paramet...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of function spaces 2022-09, Vol.2022, p.1-10
Hauptverfasser: Chen, Hengjie, Yu, Dansheng, Li, Zhong
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Approximation
Artificial intelligence
Artificial neural networks
Continuity (mathematics)
Mathematical analysis
Neural networks
Operators (mathematics)
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the present paper, we construct a new type of two-hidden-layer feedforward neural network operators with ReLU activation function. We estimate the rate of approximation by the new operators by using the modulus of continuity of the target function. Furthermore, we analyze features such as parameter sharing and local connectivity in this kind of network structure.
ISSN:2314-8896
2314-8888
DOI:10.1155/2022/1713912