Neural Networks for Optimal Control
ME-717
Bibliography
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- Furieri et al. - 2024 - Learning to Boost the Performance of Stable Nonlin.pdf
- Furieri et al. - 2025 - Erratum to “Learning to Boost the Performance of S.pdf
- Massai et al. Unconstrained learning of networked interconnected operators.pdf
- Saccani et al. Optimal distributed control with network of neural closed loop maps.pdf
- Arcak et al. - 2016 - Networks of Dissipative Systems.pdf
- Koelewijn et al. Incremental Stability and performance analysis of DT nonlinear systems.pdf
- Losslessness_feedback_equivalence_and_the_global_stabilization_of_discrete-time_nonlinear_systems.pdf
- Neural Port-Hamiltonian Models.pdf
- Van Der Schaft - 2017 - L2-Gain and Passivity Techniques in Nonlinear Cont.pdf
- Willems Dissipative Dynamical Systems Part 1.pdf
- Fazlyab et al. - Efficient and Accurate Estimation of Lipschitz Cons.pdf
- Resurrecting Recurrent Neural Networks for Long Sequences.pdf
- Revay et al. - 2023 - Recurrent Equilibrium Networks Flexible Dynamic M.pdf
- Structured state-space models are deep Wiener models.pdf
- Wang_Manchester_2023_Direct Parameterization of Lipschitz-Bounded Deep Networks.pdf