China University of Mining and Technology (cumt)

Lanhao Wang

Liang Dong

Xiahui Gui

Yangchao Xia

Wei Dai

Mengjie Hu

Dianhui Wang

Jing Nan

Yanshuang Ao

Guanghui Wang

Tianyuan Xu

Langlong Ji

Zhenyong Miao

Lei Wang

Yijun Cao

Qinshan Liu

Yonggai Li

Mengdi Xu

Yaowen Xing

Provided is an industrial process soft sensor method based on a federated stochastic configuration network, including: acquiring historical industrial process auxiliary data and corresponding product quality data; finding out optimal hidden layer parameters; processing, by a central server, to obtain global parameters, and downloading same to each factory as hidden layer parameters for a local model; obtaining output weights of a current network through an optimization algorithm, and uploading same to the server for weighted aggregation; and when the number of hidden layer nodes in a current network exceeds a maximum given value or a residual in current iteration meets an expected tolerance, completing modeling to obtain the global federated stochastic configuration network without adding new nodes. The present disclosure effectively improves prediction performance of models and protects data privacy, thus meeting industrial process soft sensor requirements.

Industrial process soft sensor method based on federated stochastic configuration network

Growing community of inventors

Jiangsu, China

Lanhao Wang