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

【摘要】：紅外測(cè)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(diǎn)。為了提高紅外測(cè)體溫的精度,分析了環(huán)境溫度、測(cè)量距離、發(fā)射率等對(duì)紅外測(cè)體溫精度的影響。研究了基于臀部的紅外體溫測(cè)量方法,建立了由臀部體表溫度轉(zhuǎn)化為人體實(shí)際體溫的溫度場(chǎng)擴(kuò)散模型,利用偏最小二乘法和人工神經(jīng)網(wǎng)絡(luò)對(duì)溫度場(chǎng)模型進(jìn)行優(yōu)化補(bǔ)償,有效的解決了各影響因素之間多重相關(guān)性的問(wèn)題和補(bǔ)償模型的非線性問(wèn)題,提高了系統(tǒng)的可靠性。實(shí)驗(yàn)結(jié)果表明,所提出的紅外測(cè)體溫補(bǔ)償模型測(cè)溫誤差范圍在-0.12~0.11℃,具有更高的測(cè)量精度且適應(yīng)性更強(qiáng)。
[Abstract]:In order to improve the accuracy of infrared temperature measurement , the influence of ambient temperature , measurement distance and emissivity on the accuracy of infrared temperature measurement is analyzed . The temperature field model is optimized by using partial least square method and artificial neural network . The experimental results show that the temperature measurement error range of the proposed infrared temperature measurement compensation model is - 0.12 锝，

本文編號(hào)：2041501