A kernel regression approach for identification of first order differential equations based on functional data

X. Y. Li; B. Y. Wu

doi:10.1016/j.aml.2021.107832

A kernel regression approach for identification of first order differential equations based on functional data

X. Y. Li^*
, B. Y. Wu

^*Corresponding author for this work

School of Mathematics

Changshu Institute of Technology
University of South Florida

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Differential equations models for functional data play a strong role in economics, medicine, and so on. It is challenging to identify a linear differential operator from functional data. The purpose of this letter is to find a new identification approach for differential equations models based on functional data. The effectiveness of our approaches are verified through numerical experiments.

Original language	English
Article number	107832
Journal	Applied Mathematics Letters
Volume	127
DOIs	https://doi.org/10.1016/j.aml.2021.107832
State	Published - May 2022

Keywords

Functional data analysis
Identification
Kernel method

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10.1016/j.aml.2021.107832

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title = "A kernel regression approach for identification of first order differential equations based on functional data",

abstract = "Differential equations models for functional data play a strong role in economics, medicine, and so on. It is challenging to identify a linear differential operator from functional data. The purpose of this letter is to find a new identification approach for differential equations models based on functional data. The effectiveness of our approaches are verified through numerical experiments.",

keywords = "Functional data analysis, Identification, Kernel method",

author = "Li, \{X. Y.\} and Wu, \{B. Y.\}",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

year = "2022",

month = may,

doi = "10.1016/j.aml.2021.107832",

language = "英语",

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T1 - A kernel regression approach for identification of first order differential equations based on functional data

AU - Li, X. Y.

AU - Wu, B. Y.

PY - 2022/5

Y1 - 2022/5

N2 - Differential equations models for functional data play a strong role in economics, medicine, and so on. It is challenging to identify a linear differential operator from functional data. The purpose of this letter is to find a new identification approach for differential equations models based on functional data. The effectiveness of our approaches are verified through numerical experiments.

AB - Differential equations models for functional data play a strong role in economics, medicine, and so on. It is challenging to identify a linear differential operator from functional data. The purpose of this letter is to find a new identification approach for differential equations models based on functional data. The effectiveness of our approaches are verified through numerical experiments.

KW - Functional data analysis

KW - Identification

KW - Kernel method

UR - https://www.scopus.com/pages/publications/85120998431

U2 - 10.1016/j.aml.2021.107832

DO - 10.1016/j.aml.2021.107832

M3 - 文章

AN - SCOPUS:85120998431

SN - 0893-9659

VL - 127

JO - Applied Mathematics Letters

JF - Applied Mathematics Letters

M1 - 107832

ER -

A kernel regression approach for identification of first order differential equations based on functional data

Abstract

Keywords

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