A Stopping Criterion for the Training Process of the Specific Signal Generator

Lizhi Cui,Peichao Zhao,Bingfeng Li,Shumin Fei,Xinwei Li,Keping Wang,Xuhui Bu,Yi Yang

doi:10.5755/j01.itc.50.1.27351

Lizhi Cui, Peichao Zhao + Show 6 more

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https://doi.org/10.5755/j01.itc.50.1.27351

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Abstract

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.

Highlights

As a practical tool, mathematics plays a very important role in engineering, especially in the field of signal processing which requires the aid of mathematical function to depict a specific signal
Several experiments were performed to demonstrate that the Specific Signal Generator (SSG) model could generate different signals according to the training data set
There is a tricky problem about when the training process should be stopped

Summary

Introduction

Mathematics plays a very important role in engineering, especially in the field of signal processing which requires the aid of mathematical function to depict a specific signal. There are many signals with certain probability features which cannot be described by a single specific function, such as the spectrum, chromatographic wave, electroencephalogram, seismic wave and so on. If there is a single mathematical description which could depict a set of signals with certain probability characteristics, it would solve many problems in the field of signal processing [8, 25]. The Deep Neural Networks (DNN), which can be regarded as a special mathematical description. It has an information forward structure, where the data flows forward from the network inputs through many hidden units and eventually to the output blocks [7]. It is widely used to generate different signals by fitting the independent variables and the dependent variables to a certain extent. It causes overfitting when the training samples are complex and limited

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