# Active Learning for Linear Parameter-Varying System Identification

arXiv: https://arxiv.org/abs/2005.00711 · HTML: https://mlsanity.com/paper/2005.00711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 1 of 1)

- [Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity](https://mlsanity.com/paper/2211.07092/) (2022)

## Abstract (arXiv)

Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.