v KBERG: A MatLab toolbox for nonlinear kernel-based regularization and system identification · Mirko Mazzoleni

# KBERG: A MatLab toolbox for nonlinear kernel-based regularization and system identification

### Abstract

We present KBERG, a MatLab package for nonlinear Kernel-BasEd ReGularization and system identification. The toolbox provides a complete environment for running experiments on simulated and experimental data from both static and dynamical systems. The whole identification procedure is supported: (i) data generation, (ii) excitation signals design; (iii) kernel-based estimation and (iv) evaluation of the results. One of the main differences of the proposed package with respect to existing frameworks lies in the possibility to separately define experiments, algorithms and test, then combining them as desired by the user. Once these three quantities are defined, the user can simply run all the computations with only a command, waiting for results to be analyzed. As additional noticeable feature, the toolbox fully supports the manifold regularization rationale, in addition to the standard Tikhonov one, and the possibility to compute different (but equivalent) types of solutions other than the standard one. [Paper, Code]

#### Reference

M. Mazzoleni, M. Scandella, F. Previdi, "KBERG: A MatLab toolbox for nonlinear kernel-based regularization and system identification," in 21st IFAC World Congress, vol. 53, no. 2, pp. 1231-1236, Jul. 2020, doi: 10.1016/j.ifacol.2020.12.1340 , ISSN: 2405-8963.

#### Bibtex

@article{MAZZOLENI20201231,
title = {KBERG: A MatLab toolbox for nonlinear kernel-based regularization and system identification},
journal = {IFAC-PapersOnLine},
volume = {53},
number = {2},
pages = {1231-1236},
year = {2020},
note = {21th IFAC World Congress},
issn = {2405-8963},
doi = {10.1016/j.ifacol.2020.12.1340},
author = {M. Mazzoleni and M. Scandella and F. Previdi},
keywords = {Kernel methods, System Identification}
}