  We study the problem of data-based design of multi-model linear inferential
(soft) sensors. The multi-model linear inferential sensors promise increased
prediction accuracy yet simplicity of the model structure and training. The
standard approach to the multi-model inferential sensor design consists in
three separate steps: 1) data labeling (establishing training subsets for
individual models), 2) data classification (creating a switching logic for the
models), and 3) training of individual models. There are two main issues with
this concept: a) as steps 2) & 3) are separate, discontinuities can occur when
switching between the models; b) as steps 1) & 3) are separate, data labelling
disregards the quality of the resulting model. Our contribution aims at both
the mentioned problems, where, for the problem a), we introduce a novel
SVM-based model training coupled with switching logic identification and, for
the problem b), we propose a direct optimization of data labelling. We
illustrate the proposed methodology and its benefits on an example from the
chemical engineering domain.
