Local learning based soft sensing methods succeed in coping with time-varying
characteristics of processes as well as nonlinearities in industrial plants. In this paper, a …

Local learning based soft sensing methods are effective in dealing with process
nonlinearities as well as time varying characteristics. In this paper, an anti-over-fitting …

The nonlinearities and time‐varying characteristics are two major causes of low
performance of soft sensors in process systems. Motivated of solving the two problems, this …

This paper proposes an adaptive soft sensing method based on selective ensemble of local
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …

To improve the predictive ability of soft sensors in chemical and industrial plants, the
selection of process variables and consideration of dynamics in the processes have been …

We propose a soft sensing method using local partial least squares models with adaptive
process state partition, referring to as the LPLS-APSP, which is capable of effectively …

Industrial plants often undergo different kinds of changes like variable drifts and time‐variant
problems, which may cause the degradation of soft sensors. In this paper, a spatio‐temporal …

We report the use of a soft sensor ensemble based on recursive partial least squares with a
large number of overlapping models. The proposed method uses process memory …

In this paper, a robust and mixture form of supervised probabilistic principal component
analysis model is proposed to deal with the soft sensing problem, particularly for those …

A new local‐based data regression technique named locality preserving regression (LPR) is
developed and applied for soft sensor modelling in the present study. By taking the local …