Deterministic convergence of complex mini-batch gradient learning algorithm for fully complex-valued neural networks

This paper investigates the fully complex mini-batch gradient algorithm for training complex-valued neural networks. Mini-batch gradient method has been widely used in neural network training, however, its convergence analysis is usually restricted to real-valued neural networks and of probability n...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 407; S. 185 - 193
Hauptverfasser:	Zhang, Huisheng, Zhang, Ying, Zhu, Shuai, Xu, Dongpo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 24.09.2020
Schlagworte:	Convergence Fully complex-valued neural networks Mini-batch gradient algorithm Wirtinger calculus Fully complex-valued neural networks Mini-batch gradient algorithm Wirtinger calculus Convergence
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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