公开(公告)号：US20240302341A1

公开(公告)日：2024-09-12

申请号：US18276179

申请日：2022-10-27

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian TANG ,  Heng XIA ,  Canlin CUI ,  Junfei QIAO

IPC: G01N33/00 ,  G06N20/20

CPC classification number: G01N33/0075 ,  G06N20/20

Abstract: A broad hybrid forest regression (BHFR)-based soft sensor method for DXN emission in a municipal solid waste incineration (MSWI) process, including: based on a broad learning system (BLS) framework, constructing a BHFR soft sensor model for small sample high-dimensional data by replacing a neuron with a non-differential base learner, where the BHFR soft sensor model includes a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method includes: mapping a high-dimensional feature; extracting a latent feature from a feature space of a fully connected hybrid matrix, and reducing model complexity and computation consumption based on an information measurement criterion; enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature; and constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inverse, and implementing high-precision modeling.