Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Antoulas, A. C."'
The Loewner framework is an interpolatory approach designed for approximating linear and nonlinear systems. The goal here is to extend this framework to linear parametric systems with an arbitrary number n of parameters. One main innovation establish
Externí odkaz:
http://arxiv.org/abs/2405.00495
In this study, we present a purely data-driven method that uses the Loewner framework (LF) along with nonlinear optimization techniques to infer quadratic with affine control dynamical systems that admit Volterra series (VS) representations from inpu
Externí odkaz:
http://arxiv.org/abs/2211.10635
We present a method that connects a well-established nonlinear (bilinear) identification method from time-domain data with neural network (NNs) advantages. The main challenge for fitting bilinear systems is the accurate recovery of the corresponding
Externí odkaz:
http://arxiv.org/abs/2208.10124
In this contribution, we propose a data-driven procedure to fit quadratic-bilinear surrogate models from data. Although the dynamics characterizing the original model are strongly nonlinear, we rely on lifting techniques to embed the original model i
Externí odkaz:
http://arxiv.org/abs/2112.01258
In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which was originally developed for applications to linear time-invariant sy
Externí odkaz:
http://arxiv.org/abs/2108.11870
We propose a model reduction method for LPV systems. We consider LPV state-space representations with an affine dependence on the scheduling variables. The main idea behind the proposed method is to compute the reduced order model in such a manner th
Externí odkaz:
http://arxiv.org/abs/2104.10767
This paper starts by deriving a factorization of the Loewner matrix pencil that appears in the data-driven modeling approach known as the Loewner framework and explores its consequences. The first is that the associated quadruple constructed from the
Externí odkaz:
http://arxiv.org/abs/2103.09674
In this paper, we address an extension of the Loewner framework for learning quadratic control systems from input-output data. The proposed method first constructs a reduced-order linear model from measurements of the classical transfer function. The
Externí odkaz:
http://arxiv.org/abs/2012.02075
In this paper, we address extensions of the Loewner Data-Driven Control (L-DDC) methodology. First, this approach is extended by incorporating two alternative approximation methods known as Adaptive-Antoulas-Anderson (AAA) and Vector Fitting (VF). Th
Externí odkaz:
http://arxiv.org/abs/2011.14950
In this work, we propose an extensive numerical study on approximating the absolute value function. The methods presented in this paper compute approximants in the form of rational functions and have been proposed relatively recently, e.g., the Loewn
Externí odkaz:
http://arxiv.org/abs/2005.02736