%0 Book %1 pillonetto2022regularized %A Pillonetto, Gianluigi %A Chen, Tianshi %A Chiuso, Alessandro %A De Nicolao, Giuseppe %A Ljung, Lennart %B Communications and Control Engineering %C Cham, Switzerland %D 2022 %I Springer %K 93-02-systems-theory-control-research-exposition 93b30-system-identification 93c05-linear-control-systems 93c10-nonlinear-control-systems %R 10.1007/978-3-030-95860-2 %T Regularized system identification. Learning dynamic models from data %U https://link.springer.com/book/10.1007/978-3-030-95860-2 %X Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniques Careful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learning Develops system identification principles in both deterministic and stochastic (Bayesian) settings This book is open access, which means that you have free and unlimited access.

@book{pillonetto2022regularized, abstract = {Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniques Careful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learning Develops system identification principles in both deterministic and stochastic (Bayesian) settings This book is open access, which means that you have free and unlimited access.}, added-at = {2022-06-24T04:05:41.000+0200}, address = {Cham, Switzerland}, author = {Pillonetto, Gianluigi and Chen, Tianshi and Chiuso, Alessandro and De Nicolao, Giuseppe and Ljung, Lennart}, biburl = {https://www.bibsonomy.org/bibtex/20629b32bf5c064abd9764dda91cc1dd4/gdmcbain}, doi = {10.1007/978-3-030-95860-2}, interhash = {a2b185479233924cd009b39b10ac6450}, intrahash = {0629b32bf5c064abd9764dda91cc1dd4}, keywords = {93-02-systems-theory-control-research-exposition 93b30-system-identification 93c05-linear-control-systems 93c10-nonlinear-control-systems}, publisher = {Springer}, series = {Communications and Control Engineering}, timestamp = {2022-06-24T04:09:53.000+0200}, title = {Regularized system identification. Learning dynamic models from data}, url = {https://link.springer.com/book/10.1007/978-3-030-95860-2}, year = 2022 }