GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment...

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Bibliographic Details
Published inInternational journal of information technology and web engineering Vol. 19; no. 1; pp. 1 - 14
Main Authors Aifen, Ye, Shuwan, Lin, Huan, Wang
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2024
Subjects
Accuracy
Back propagation
Back propagation networks
Cooling
Decision making
Design optimization
Electric currents
Error analysis
Error correction
Genetic algorithms
Genetic engineering
Heat
Information technology
Local optimization
Machine learning
Measuring instruments
Neural networks
Optimization algorithms
Propagation
Temperature compensation
Temperature measurement
Temperature measuring instruments
Thermoelectricity
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Summary:Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.
ISSN:1554-1045
1554-1053
DOI:10.4018/IJITWE.337491