Searched for: +
(1 - 4 of 4)
document
Simha, A. (author), Kaparin, Vadim (author), Mullari, Tanel (author), Kotta, Ulle (author)
—This technical note addresses the problem of transforming a single-input–single-output discrete-time system into the extended observer form, which comprise a linear time-invariant observable component, and a nonlinear injection term, which depends on the input, output, and their forward shifts up to a finite order. Intrinsic necessary and...
journal article 2023
document
Mey, A. (author), Loog, M. (author)
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at our disposal. This survey covers theoretical results for this setting and maps out the benefits of unlabeled data in classification and regression tasks. Most methods that use unlabeled data rely on certain assumptions about the data...
journal article 2022
document
Delimpaltadakis, Giannis (author), Mazo, M. (author)
In this work, we derive a region-based self-triggered control (STC) scheme for nonlinear systems with bounded disturbances and model uncertainties. The proposed STC scheme is able to guarantee different performance specifications (e.g. stability, boundedness, etc.), depending on the event-triggered control (ETC) triggering function that is...
journal article 2021
document
Zhou, Zixia (author), Zu, Xinrui (author), Wang, Yuanyuan (author), Lelieveldt, Boudewijn P.F. (author), Tao, Q. (author)
Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a...
journal article 2021
Searched for: +
(1 - 4 of 4)