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Your search keyword '"Scandella, Matteo"' showing total 2 results
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2 results on '"Scandella, Matteo"'

1. Kernel-based identification of asymptotically stable continuous-time linear dynamical systems.

Author

Scandella, Matteo, Mazzoleni, Mirko, Formentin, Simone, and Previdi, Fabio

Subjects
LYAPUNOV stability, LINEAR dynamical systems, TRANSFER functions, IDENTIFICATION
Abstract

In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. However, for such models, model selection is a hard task if no prior physical knowledge is given. In this paper, we propose a non-parametric approach to infer a continuous-time linear model from data, by automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. By means of benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, also in the critical case when short sequences of canonical input signals, like impulses or steps, are used for model learning. [ABSTRACT FROM AUTHOR]

Published
2022
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2. A Note on the Numerical Solutions of Kernel-Based Learning Problems.

Author

Scandella, Matteo, Mazzoleni, Mirko, Formentin, Simone, and Previdi, Fabio

Subjects
LEARNING problems, MATRIX inversion, SYSTEM identification, DYNAMICAL systems, SYMMETRIC matrices
Abstract

In the last decade, kernel-based learning approaches typically employed for classification and regression have shown outstanding performance also in dynamic system identification. The typical way to compute the solution of this learning problem subsumes the inversion of the kernel matrix. However, due to limited machine precision, this might not be possible in many practical applications. In this article, we analyze the aforementioned problem and show that the typical estimate is just one of the possible infinite solutions that can be leveraged, considering both the supervised and the semisupervised settings. We show under which conditions the infinite solutions are equivalent, and if it is not the case, we provide a bound on the mismatch between two generic solutions. Then, we propose two specific solutions that are particularly suited to boost sparsity or performance. [ABSTRACT FROM AUTHOR]

Published
2021
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