Non-parametric Gaussian process kernel DMD and LS-SVM predictors revisited A unifying approach

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

P. Lopes dos Santos (Autor) FEUP; Azevedo-Perdicoulis, TP (Autor) Outra; Salgado, PA (Autor) Outra

Ata de Conferência Internacional

Título: IFAC-PapersOnLine

22nd IFAC World Congress

Yokohama, 9 July 2023 through 14 July 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

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ScienceDirect (Elsevier)

Outras Informações

ID Authenticus: P-00Z-TDP

DOI: 10.1016/j.ifacol.2023.10.1075

Abstract (EN): In this work, the prediction of a time series is formulated as a gaussian process regression, for different levels of noise. The gaussian regressor is translated into lower rank Dynamic Mode Decomposition methods that use kernels (K-DMD) - Kernel regression and Least Squares Support Vector Machines. The presented unified approach delivers an algorithm where the optimisation of the marginal likelihood function can be used to find the parameters of the kernel regression. The viability of the procedure is demonstrated on a chaotic series, with quite good adjustment results being obtained. Copyright (c) 2023 The Authors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7