# Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic

arXiv: https://arxiv.org/abs/2206.08961 · HTML: https://mlsanity.com/paper/2206.08961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Data-based Design of Inferential Sensors for Petrochemical Industry](https://mlsanity.com/paper/2106.13503/) (2021)

## Abstract (arXiv)

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.