Maximum entropy properties of discrete-time first-order stable spline kernel

DOI10.1016/j.automatica.2015.12.009zbMath1335.93131arXiv1504.03253OpenAlexW1900832178MaRDI QIDQ254525

Francesca P. Carli, Alessandro Chiuso, Gianluigi Pillonetto, Tohid Ardeshiri, Tianshi Chen, Lennart Ljung

Publication date: 8 March 2016

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.03253

zbMATH Keywords

system identificationmaximum entropyregularization methodkernel structure

Mathematics Subject Classification ID

Sampled-data control/observation systems (93C57) Identification in stochastic control theory (93E12) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25)

Related Items (15)

Linear system identification using the sequential stabilizing spline algorithm ⋮ On kernel design for regularized LTI system identification ⋮ On semiseparable kernels and efficient implementation for regularized system identification and function estimation ⋮ Maximum entropy vector kernels for MIMO system identification ⋮ A new kernel-based approach to system identification with quantized output data ⋮ When cannot regularization improve the least squares estimate in the kernel-based regularized system identification ⋮ Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel ⋮ On input design for regularized LTI system identification: power-constrained input ⋮ Kernel Absolute Summability Is Sufficient but Not Necessary for RKHS Stability ⋮ Sparse plus low rank network identification: a nonparametric approach ⋮ On asymptotic properties of hyperparameter estimators for kernel-based regularization methods ⋮ A shift in paradigm for system identification ⋮ Sparse estimation in linear dynamic networks using the stable spline horseshoe prior ⋮ Kernel-based identification of asymptotically stable continuous-time linear dynamical systems ⋮ On embeddings and inverse embeddings of input design for regularized system identification

Cites Work

• Unnamed Item
• Kernel methods in system identification, machine learning and function estimation: a survey
• Prediction error identification of linear systems: a nonparametric Gaussian regression approach
• A new kernel-based approach for linear system identification
• Classes of linear operators. Vol. II
• Implementation of algorithms for tuning parameters in regularized least squares problems in system identification
• On the estimation of transfer functions, regularizations and Gaussian processes-revisited
• System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques
• Maximum Entropy Kernels for System Identification

This page was built for publication: Maximum entropy properties of discrete-time first-order stable spline kernel