本文選題：偏最小二乘法 + 人工神經(jīng)網(wǎng)絡(luò)��； 參考：《電子測量與儀器學報》2017年09期

【摘要】：紅外測體溫的精度受到多種因素的影響,具有非線性和高度復(fù)雜性的特點。為了提高紅外測體溫的精度,分析了環(huán)境溫度、測量距離、發(fā)射率等對紅外測體溫精度的影響。研究了基于臀部的紅外體溫測量方法,建立了由臀部體表溫度轉(zhuǎn)化為人體實際體溫的溫度場擴散模型,利用偏最小二乘法和人工神經(jīng)網(wǎng)絡(luò)對溫度場模型進行優(yōu)化補償,有效的解決了各影響因素之間多重相關(guān)性的問題和補償模型的非線性問題,提高了系統(tǒng)的可靠性。實驗結(jié)果表明,所提出的紅外測體溫補償模型測溫誤差范圍在-0.12~0.11℃,具有更高的測量精度且適應(yīng)性更強。
[Abstract]:The accuracy of infrared temperature measurement is influenced by many factors and has the characteristics of nonlinearity and high complexity. In order to improve the accuracy of infrared temperature measurement, the effects of ambient temperature, measuring distance and emissivity on the accuracy of infrared temperature measurement are analyzed. The infrared temperature measurement method based on buttocks is studied, and the diffusion model of temperature field is established, which is transformed from hip surface temperature to actual body temperature. The partial least square method and artificial neural network are used to optimize and compensate the temperature field model. It effectively solves the problem of multiple correlation among various factors and the nonlinear problem of compensation model, and improves the reliability of the system. The experimental results show that the temperature measurement error range of the proposed infrared temperature compensation model is -0.12 ~ 0.11 鈩，

本文編號：2041502