Improved Virtual Sample Generation Method Using Enhanced Conditional Generative Adversarial Networks with Cycle Structures for Soft Sensors with Limited Data

摘要

In the modern chemical industry,only a small number of representative samples can be used to build soft models due to practical factors.However,the accuracy of the soft model built in this case is not [ sufficient to meet the demand.To overcome this problem,a novel virtual sample generation(VSG)method based on conditional generative adversarial networks(CGANs)with a cycle structure(CS-CGAN)is proposed to augment the sample data sets and enrich the sample diversity.In the proposed method,first,for obtaining the inputs of virtual samples,the Wasserstein GAN with gradient penalty(WGAN-GP)is used to generate new samples x based on the original sample distribution to fill the scarcity regions of the data.Second,the reasonable outputs of the newly generated samples are determined by the CS-CGAN with consistency test.To verify the performance of the proposed new method,numerical simulations and real-world data sets are used.The results show that the proposed new method can effectively generate realistic samples and outperform other methods in improving the performance of soft sensors.