Universal approximation theorem for vector- and hypercomplex-valued neural networks-Reference-Cited by-同舟云学术

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Universal approximation theorem for vector- and hypercomplex-valued neural networks

Published:2024-12 Issue: Volume:180 Page:106632
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Valle Marcos Eduardo^ORCID,Vital Wington L.^ORCID,Vieira Guilherme^ORCID

Publisher

Elsevier BV

Reference63 articles.

1. Complex-valued neural networks with multi-valued neurons;Aizenberg,2011

2. Negative results for approximation using single layer and multilayer feedforward neural networks;Almira;Journal of Mathematical Analysis and Applications,2021

3. Multilayer perceptrons to approximate quaternion valued functions;Arena;Neural Networks,1997

4. Neural networks in multidimensional domains: Fundamentals and new trends in modeling and control;Arena,1998

5. The octonions;Baez;American Mathematical Society. Bulletin,2002

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards Explaining Hypercomplex Neural Networks;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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