A virtual sample generation approach based on a modified conditional GAN and centroidal Voronoi tessellation sampling to cope with small sample size problems: Application to soft sensing for chemical process

Autor: Kun-Rui Hou, Zhong-Sheng Chen, Qunxiong Zhu, Yuan Xu, Mei-Yu Zhu
Rok vydání: 2021
Předmět:
Zdroj: Applied Soft Computing. 101:107070
ISSN: 1568-4946
Popis: In the chemical industries, it is occasionally hard to acquire plenty of samples for developing a soft sensor due to physical limitations and high cost of measurements. To overcome this issue, we come up with a virtual sample generation approach to synthesis new samples to rationally enlarge training sets for soft sensing. Firstly, by applying the centroidal Voronoi tessellation sampling, uniformly distributed new samples x are obtained, for the sake of as possible filling up data scarcity regions. Secondly, the corresponding output of those new samples is determined by the conditional distribution P ( y | x ) captured by a modified conditional GAN implicitly. The negative logarithmic prediction density is then taken to be a measure of closeness between generated samples and real samples. To examine the effectiveness of our approach, numerical simulations over a benchmarking function and a chemical process application were carried out. Experimental results suggested that in contrast to other existing state-of-the-art approaches, our approach can yield more authentic samples but also give rise to significant improvement in soft sensor’s performance.
Databáze: OpenAIRE
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