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

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Information Technology and Web Engineering Pub Date : 2024-02-07 DOI:10.4018/ijitwe.337491
Ye Aifen, Shuwan Lin, Wang Huan
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Abstract

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.
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利用混合机器学习算法对热电堆温度补偿进行 GA-BP 优化
热电堆采用非接触式红外测温原理,被广泛应用于各种精密温度测量仪器中。本文分析了环境温度对热电堆测量精度的影响,并提出了一种基于 GA-BP(遗传算法-反向传播)神经网络的环境温度补偿方法。GA 算法弥补了 BP 算法迭代速度慢、易陷入局部优化的缺陷。实验仿真结果表明,基于 GA-BP 的环境温度补偿能够准确修正由环境温度和其他因素引起的测量误差。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
24
期刊介绍: Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.
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