.png){kind=icon}
返回
Deterministic convergence of complex mini-batch gradient learning algorithm for fully complex-valued neural networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2020.04.114.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 nature. By introducing a new Taylor mean value theorem for analytic functions, in this paper we establish determin-istic convergence results for the fully complex mini-batch gradient algorithm under mild conditions. The deterministic convergence here means that the algorithm will deterministically converge, and both the weak convergence and strong convergence will be proved. Benefited from the newly introduced mean value theorem, our results are of global nature in that they are valid for arbitrarily given initial values of the weights. The theoretical findings are validated with a simulation example. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Fully complex-valued neural networks
Mini-batch gradient algorithm
Convergence
Wirtinger calculus
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence与信号相干相关的复值前馈神经网络的泛化特性
PREAI
分享收藏Theoretical analysis of batch and on-line training for gradient descent learning in neural networks.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Is a Complex-Valued Stepsize Advantageous in Complex-Valued Gradient Learning Algorithms?复值步长在复值梯度学习算法中是有利的吗？
PREAI
分享收藏
查看更多内容