Regularized System Identification Learning Dynamic Models from Data

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learn...

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Xehetasun bibliografikoak
Egile nagusia:	Pillonetto, Gianluigi (auth)
Beste egile batzuk:	Chen, Tianshi (auth), Chiuso, Alessandro (auth), De Nicolao, Giuseppe (auth), Ljung, Lennart (auth)
Formatua:	Baliabide elektronikoa Liburu kapitulua
Hizkuntza:	ingelesa
Argitaratua:	Cham Springer Nature 2022
Saila:	Communications and Control Engineering
Gaiak:	Machine learning Automatic control engineering Statistical physics Bayesian inference Probability & statistics Cybernetics & systems theory System Identification Machine Learning Linear Dynamical Systems Nonlinear Dynamical Systems Kernel-based Regularization Bayesian Interpretation of Regularization Gaussian Processes Reproducing Kernel Hilbert Spaces Estimation Theory Support Vector Machines Regularization Networks
Sarrera elektronikoa:	OAPEN Library: download the publication OAPEN Library: description of the publication
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Regularized System Identification Learning Dynamic Models from Data

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