The Stochastic Occupation Kernel Method for System Identification

Autor:	Wells, Michael, Lahouel, Kamel, Jedynak, Bruno
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	The method of occupation kernels has been used to learn ordinary differential equations from data in a non-parametric way. We propose a two-step method for learning the drift and diffusion of a stochastic differential equation given snapshots of the process. In the first step, we learn the drift by applying the occupation kernel algorithm to the expected value of the process. In the second step, we learn the diffusion given the drift using a semi-definite program. Specifically, we learn the diffusion squared as a non-negative function in a RKHS associated with the square of a kernel. We present examples and simulations. Comment: 8 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.15661 Zobrazit plný text záznamu View this record from Arxiv