Soft sensing of a nonlinear and multimode processes based on semi-supervised weighted Gaussian regression
US10678196B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 27, 2020 |
| Grant date | Jun 9, 2020 |
| Priority date | — |
| Expiry date | Jan 27, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Soft sensing of nonlinear and multimode industrial processes given a limited number of labeled data samples is disclosed. Methods include a semi-supervised probabilistic density-based regression approach, called Semi-supervised Weighted Gaussian Regression (SWGR). In SWGR, different weights are assigned to each training sample based on their similarities to a query sample. Then a local weighted Gaussian density is built for capturing the joint probability of historical samples around the query sample. The training process of parameters in SWGR incorporates both labeled and unlabeled data samples via a maximum likelihood estimation algorithm. In this way, the soft sensor model is able to approximate the nonlinear mechanics of input and output variables and remedy the insufficiency of labeled samples. At last, the output prediction as well as the uncertainty of prediction can be obtained by the conditional distribution.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.