文章基本信息

标题：New Features in the System Identification Toolbox - Rapprochements with Machine Learning
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作者：Khaled F. Aljanaideh ; Debraj Bhattacharjee ; Rajiv Singh 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：369-373
DOI：10.1016/j.ifacol.2021.08.387
语种：English
出版社：Elsevier
摘要：AbstractThe R2021b release of the System Identification Toolbox™ for MATLAB® contains new features that enable the use of machine learning techniques for nonlinear system identification. With this release it is possible to build nonlinear ARX models with regression tree ensemble and Gaussian process regression mapping functions. The release contains several other enhancements including, but not limited to, (a) online state estimation using the extended Kalman filter and the unscented Kalman filter with code generation capability; (b) improved handling of initial conditions for transfer functions and polynomial models; (c) a new architecture of nonlinear black-box models that streamlines regressor handling, reduces memory footprint and improves numerical accuracy; and (d) easy incorporation of identification apps in teaching tools and interactive examples by leveraging the Live Editor tasks of MATLAB.
关键词：KeywordsMATLABSystem Identification Toolboxsystem identificationmachine learning