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Found 11 papers, 4 papers with code
A computationally lightweight safe learning algorithm
Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems.
Neural oscillators for magnetic hysteresis modeling
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems.
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control.
Kernel-based identification using Lebesgue-sampled data
Sampling in control applications is increasingly done non-equidistantly in time.
Discovery of sparse hysteresis models for piezoelectric materials
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques.
Decoupling multivariate functions using a nonparametric filtered tensor decomposition
Multivariate functions emerge naturally in a wide variety of data-driven models.
Decoupling multivariate functions using a non-parametric Filtered CPD approach
Usually a generic basis function expansion is used, e. g. a polynomial basis, and the parameters of the function are tuned given the data.
Deep Convolutional Networks in System Identification
Recent developments within deep learning are relevant for nonlinear system identification problems.
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
