Gaussian Processes-based Parametric Identification for Dynamical Systems

被引：1

作者：

Benosman, Mouhacine ^{[1
]}

Farahmand, Amir-massoud ^{[1
]}

机构：

[1] MERL, Cambridge, MA 02139 USA

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

关键词：

DIFFERENTIAL-EQUATIONS; MODEL;

D O I：

10.1016/j.ifacol.2017.08.2431

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we present some results on parametric identification for dynamical systems. More specifically, we consider the general case of dynamics described by partial differential equations (PDEs), which includes the special case of ordinary differential equations (ODES). We follow a stochastic approach and formulate the identification problem as a Gaussian process optimization with respect to the unknown parameters of the PDE. We use proper orthogonal decomposition (POD) model reduction theory together with a data-driven Gaussian Process Upper Confidence Bound (GP-UCB), to solve the identification problem. The proposed approach is validated on the coupled Burgers' equation benchmark. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：14034 / 14039

页数：6

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