Copertina

Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	K., Lakshmi Priya P., Schwung, Andreas
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems
di: Yuwono, Steve, et al.
Pubblicazione: (2024)

Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
di: Pöppelbaum, Johannes, et al.
Pubblicazione: (2024)

Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games
di: Yuwono, Steve, et al.
Pubblicazione: (2024)

Stabilizing Multimodal Autoencoders: A Theoretical and Empirical Analysis of Fusion Strategies
di: Altinses, Diyar, et al.
Pubblicazione: (2025)

Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems
di: Yuwono, Steve, et al.
Pubblicazione: (2024)

Layer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning
di: Altinses, Diyar, et al.
Pubblicazione: (2026)

Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning
di: Lassoued, Sofiene, et al.
Pubblicazione: (2024)

Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems
di: Yuwono, Steve, et al.
Pubblicazione: (2024)

Improving Quaternion Neural Networks with Quaternionic Activation Functions
di: Pöppelbaum, Johannes, et al.
Pubblicazione: (2024)

Prior-Informed Neural Network Initialization: A Spectral Approach for Function Parameterizing Architectures
di: Torres, David Orlando Salazar, et al.
Pubblicazione: (2026)

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach
di: Ilic, Sasa, et al.
Pubblicazione: (2024)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
di: Kim, Youngseok, et al.
Pubblicazione: (2022)

Transfer learning for functional linear regression via control variates
di: Yang, Yuping, et al.
Pubblicazione: (2026)

Anomaly Detection using Ensemble Classification and Evidence Theory
di: Arévalo, Fernando, et al.
Pubblicazione: (2022)

Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows
di: Rostamijavanani, Abdolvahhab, et al.
Pubblicazione: (2025)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
di: Noskov, Fedor, et al.
Pubblicazione: (2025)

Information Fusion for Assistance Systems in Production Assessment
di: Arévalo, Fernando, et al.
Pubblicazione: (2023)

Data-driven system identification using quadratic embeddings of nonlinear dynamics
di: Klus, Stefan, et al.
Pubblicazione: (2025)

Meta-learning of shared linear representations beyond well-specified linear regression
di: Even, Mathieu, et al.
Pubblicazione: (2025)

Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition
di: Saito, Akira, et al.
Pubblicazione: (2026)

Augmented balancing weights as linear regression
di: Bruns-Smith, David, et al.
Pubblicazione: (2023)

Semi-supervised learning for linear extremile regression
di: Jiang, Rong, et al.
Pubblicazione: (2025)

Data/moment-driven approaches for fast predictive control of collective dynamics
di: Albi, Giacomo, et al.
Pubblicazione: (2024)

Deep learning architectures for data-driven damage detection in nonlinear dynamic systems
di: Joseph, Harrish, et al.
Pubblicazione: (2024)

Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems
di: Benbouzid, Djalel, et al.
Pubblicazione: (2024)

Multifidelity Gaussian process regression for solving nonlinear partial differential equations
di: El-Boukkouri, Fatima-Zahrae, et al.
Pubblicazione: (2026)

An L-BFGS-B approach for linear and nonlinear system identification under $\ell_1$ and group-Lasso regularization
di: Bemporad, Alberto
Pubblicazione: (2024)

Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
di: Evangelou, Nikolaos, et al.
Pubblicazione: (2024)

A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression
di: Castillo, Ismaël, et al.
Pubblicazione: (2024)

Deep linear networks for regression are implicitly regularized towards flat minima
di: Marion, Pierre, et al.
Pubblicazione: (2024)

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
di: Strässer, Robin, et al.
Pubblicazione: (2024)

Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
di: Nair, Lakshmi
Pubblicazione: (2024)

Generalisation and benign over-fitting for linear regression onto random functional covariates
di: Jones, Andrew, et al.
Pubblicazione: (2025)

Time-optimal neural feedback control of nilpotent systems as a binary classification problem
di: Bicego, Sara, et al.
Pubblicazione: (2025)

Generative AI-driven forecasting of oil production
di: Gandhi, Yash, et al.
Pubblicazione: (2024)

Closed-form congestion control via deep symbolic regression
di: Martins, Jean, et al.
Pubblicazione: (2024)

Data-driven approaches to inverse problems
di: Schönlieb, Carola-Bibiane, et al.
Pubblicazione: (2025)

Fast approximative estimation of conditional Shapley values when using a linear regression model or a polynomial regression model
di: Aanes, Fredrik Lohne
Pubblicazione: (2025)

Non-parametric regression for robot learning on manifolds
di: Lopez-Custodio, P. C., et al.
Pubblicazione: (2023)

Discovery and inference beyond linearity by integrating Bayesian regression, tree ensembles and Shapley values
di: Spadaccini, Giorgio, et al.
Pubblicazione: (2025)