Identification of parametric models from experimental data

Identification of parametric models: fro

📁 Identification of parametric models: from experimental data

✍ Walter E., Pronzato L. 📂 Library 📅 1997 🏛 Springer 🌐 English

The presentation of a coherent methodology for the estimation of the parameters of mathematical models from experimental data is examined in this volume. Many topics are covered including the choice of the structure of the mathematical model, the choice of a performance criterion to compare models,

Identifiability of Parametric Models

📁 Identifiability of Parametric Models

✍ Eric Walter 📂 Library 📅 1987 🏛 Elsevier Ltd, Pergamon Press 🌐 English

Identification of Continuous-time Models

📁 Identification of Continuous-time Models from Sampled Data

✍ Hugues Garnier, Liuping Wang, Peter C. Young (auth.), Hugues Garnier PhD, Liupin 📂 Library 📅 2008 🏛 Springer-Verlag London 🌐 English

<p><P>System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, m

Identification of continuous-time models

📁 Identification of continuous-time models from sampled data

✍ Hugues Garnier, Liuping Wang, Peter C. Young (auth.), Hugues Garnier PhD, Liupin 📂 Library 📅 2008 🏛 Springer-Verlag London 🌐 English

<p><P>System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, m

Identification of Continuous-time Models

📁 Identification of Continuous-time Models from Sampled Data

✍ Hugues Garnier, Liuping Wang, Peter C. Young (auth.), Hugues Garnier PhD, Liupin 📂 Library 📅 2008 🏛 Springer-Verlag London 🌐 English

Regularized System Identification Learni

📁 Regularized System Identification Learning Dynamic Models from Data

✍ Gianluigi Pillonetto 📂 Library 📅 2022 🏛 Springer International Publishing AG 🌐 English

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