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Found 7 papers, 1 papers with code
On the reduction of Linear Parameter-Varying State-Space models
This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance.
Learning flow functions of spiking systems
We propose a framework for surrogate modelling of spiking systems.
Electrical Fault Localisation Over a Distributed Parameter Transmission Line
Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable.
Universal approximation of flows of control systems by recurrent neural networks
In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs.
Learning Flow Functions from Data with Applications to Nonlinear Oscillators
We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance.
Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection.
Oscillations in Mixed-Feedback Systems
The calculation of the limit cycle is reformulated as the zero finding of a mixed-monotone relation, that is, of the difference of two maximally monotone relations.
