Search Results for author:
Found 22 papers, 5 papers with code
Learning-based model augmentation with LFRs
This model structure is able to represent many common model augmentation structures, thus unifying them under the proposed model structure.
State Derivative Normalization for Continuous-Time Deep Neural Networks
However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods.
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions.
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems.
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data.
Computationally efficient predictive control based on ANN state-space models
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models.
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions.
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies
Our numerical results show that the proposed data-driven pricing scheme can effectively align the users' flows with the system optimum, significantly reducing the societal costs with respect to the uncontrolled flows (by about 15% and 25% depending on the scenario), and respond to environmental changes in a robust and efficient manner.
Deep Subspace Encoders for Nonlinear System Identification
In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation.
Koopman Form of Nonlinear Systems with Inputs
In applications for systems with inputs, generally a linear time invariant (LTI) form of the Koopman model is assumed, as it facilitates the use of control techniques such as linear quadratic regulation and model predictive control.
Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs
In the lifted space, the dynamics are linear and represented by a so-called Koopman operator.
Continuous-time identification of dynamic state-space models by deep subspace encoding
Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models.
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models.
Variational message passing for online polynomial NARMAX identification
We propose a variational Bayesian inference procedure for online nonlinear system identification.
Deep Identification of Nonlinear Systems in Koopman Form
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders.
Improved Initialization of State-Space Artificial Neural Networks
The identification of black-box nonlinear state-space models requires a flexible representation of the state and output equation.
Nonlinear state-space identification using deep encoder networks
This paper introduces a method that approximates the simulation loss by splitting the data set into multiple independent sections similar to the multiple shooting method.
System identification of biophysical neuronal models
After sixty years of quantitative biophysical modeling of neurons, the identification of neuronal dynamics from input-output data remains a challenging problem, primarily due to the inherently nonlinear nature of excitable behaviors.
Non-linear State-space Model Identification from Video Data using Deep Encoders
An encoder function, represented by a neural network, is introduced to learn a reconstructability map to estimate the model states from past inputs and outputs.
Feedback Identification of conductance-based models
This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise.
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems
Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models.
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming
Based on the results achieved for the case studies, we critically analyse the performance of the proposed method.
