Optimization algorithm for spectral emissivity model

Chun Ling Yang; Chao Liu; Jing Min Dai

Optimization algorithm for spectral emissivity model

Chun Ling Yang^*
, Chao Liu
, Jing Min Dai

^*Corresponding author for this work

School of Electrical Engineering and Automation, Harbin Institute of Technology

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

2 Scopus citations

Abstract

In order to identify continuous emissivities and true temperature from multispectral neural network emissivity model, a new optimization algorithm which is a single parameter dynamic search and golden section hybrid algorithm was designed. It is unnecessary to get second derivative of target function in this algorithm, and it is simple and easy to program. The simulation experiment show that this algorithm has both fast constringency speed and high measurement precision.

Original language	English
Pages (from-to)	1114-1116
Number of pages	3
Journal	Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology
Volume	35
Issue number	9
State	Published - Sep 2003
Externally published	Yes

Keywords

Continuous spectral emissivity
Multispectral
Neural network
Optimization algorithm

Cite this

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title = "Optimization algorithm for spectral emissivity model",

abstract = "In order to identify continuous emissivities and true temperature from multispectral neural network emissivity model, a new optimization algorithm which is a single parameter dynamic search and golden section hybrid algorithm was designed. It is unnecessary to get second derivative of target function in this algorithm, and it is simple and easy to program. The simulation experiment show that this algorithm has both fast constringency speed and high measurement precision.",

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author = "Yang, \{Chun Ling\} and Chao Liu and Dai, \{Jing Min\}",

year = "2003",

month = sep,

language = "英语",

volume = "35",

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journal = "Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology",

issn = "0367-6234",

publisher = "Harbin Institute of Technology",

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T1 - Optimization algorithm for spectral emissivity model

AU - Yang, Chun Ling

AU - Liu, Chao

AU - Dai, Jing Min

PY - 2003/9

Y1 - 2003/9

N2 - In order to identify continuous emissivities and true temperature from multispectral neural network emissivity model, a new optimization algorithm which is a single parameter dynamic search and golden section hybrid algorithm was designed. It is unnecessary to get second derivative of target function in this algorithm, and it is simple and easy to program. The simulation experiment show that this algorithm has both fast constringency speed and high measurement precision.

AB - In order to identify continuous emissivities and true temperature from multispectral neural network emissivity model, a new optimization algorithm which is a single parameter dynamic search and golden section hybrid algorithm was designed. It is unnecessary to get second derivative of target function in this algorithm, and it is simple and easy to program. The simulation experiment show that this algorithm has both fast constringency speed and high measurement precision.

KW - Continuous spectral emissivity

KW - Multispectral

KW - Neural network

KW - Optimization algorithm

UR - https://www.scopus.com/pages/publications/1642494976

M3 - 文章

AN - SCOPUS:1642494976

SN - 0367-6234

VL - 35

SP - 1114

EP - 1116

JO - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

JF - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

IS - 9

ER -

Optimization algorithm for spectral emissivity model

Abstract

Keywords

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