基于分位数回归CGAN的虚拟样本生成方法及其过程建模应用

Complex industry processes suffer from the problems on data scarcity of training samples which are collected for modeling, as a result of inaccessibility of difficult-to-measure variables or high cost in time or economy. To tackle the issues, we proposed a novel virtual sample generation embedding quantile regression into conditional generative adversarial networks (QRCGAN). First of all, a regression is embedded in the standard CGAN "generator-discriminator" two-element game structure, so that the model not only has the ability to generate label samples, but also has the ability to handle regression prediction problems. Secondly, the regressor is implemented by the quantile regression neural network (QRNN), together with the discriminator and generator for simultaneous adversarial training. Once the model reaches the Nash equilibrium, with the help of the QRNN regressor, the generator can generate new samples that fall within a certain confidence interval. Moreover, the Kullback-Leibler (KL) divergence was used to evaluate the quality of the generated samples. Finally, the effectiveness of the proposed method is verified by standard function data and actual chemical process data.