Master Dynamic Models From Data With Regularized System Identification Techniques: A Practical Kernel Based Guide For Engineers And Data Scientists Today

Dynamic System Models from Data with Regularized Kernel
✔️ Open access kernel-based system identification guide, 404 pages

This open-access book explains kernel-based regularized identification and how it helps learn dynamic models from data while keeping system-theoretic principles intact. It targets engineers, data scientists, and researchers in control, machine learning and statistics.

✅ Author: Chen, Tianshi
✅ Edition: 1st ed. 2022
✅ Pages: 404
✅ Language: English
✅ Open access
✅ Release date: 14-05-2022

💡 What is kernel-based regularized system identification and why is it a handy choice for building dynamic models?

It blends data-driven learning with system theory to estimate dynamic models safely and efficiently.

- kernel-based identification
- regularization theory
- dynamic modeling from data
- control systems and data science applications
✝️