文章基本信息

标题：A Stopping Criterion for the Training Process of the Specific Signal Generator
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作者：Lizhi Cui ; Peichao Zhao ; Bingfeng Li 等
期刊名称：European Integration Studies
印刷版ISSN：2335-8831
出版年度：2021
卷号：50
期号：1
页码：153-170
DOI：10.5755/j01.itc.50.1.27351
语种：English
出版社：Kaunas University of Technology
摘要：Mathematical description of a complex signal is very important in engineering but nearly impossible in many occasions. The emergence of the Generative Adversarial Network (GAN) shows the possibility to train a single neural network to be a Specific Signal Generator (SSG), which is only controlled by a random vector with several elements. However, there is no explicit criterion for the GAN training process to stop, and in real applications the training always stops after a certain big iteration. In this paper, a serious issue was discussed during the process to use GAN as a SSG. And, an explicit criterion for the GAN as a SSG to stop the training process were proposed. Several experiments were carried out to illustrate the issues mentioned above and the effectiveness of the stopping criterion proposed in this paper.
关键词：Generative Adversarial Network;Specific Signal Generator;Stopping Criterion