本文選題：果蠅算法 + RBF神經(jīng)網(wǎng)絡(luò)�。� 參考：《傳感技術(shù)學(xué)報(bào)》2017年11期

【摘要】：針對(duì)應(yīng)變片式銷軸傳感器井下工作過(guò)程中溫度發(fā)生變化產(chǎn)生溫度漂移,導(dǎo)致測(cè)量精度降低的問(wèn)題,提出一種果蠅算法優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)的溫度補(bǔ)償模型,采用果蠅算法對(duì)神經(jīng)網(wǎng)絡(luò)的擴(kuò)展參數(shù)進(jìn)行全局優(yōu)化,利用應(yīng)力測(cè)試平臺(tái)實(shí)測(cè)參數(shù)及神經(jīng)網(wǎng)絡(luò)非線性映射能力訓(xùn)練溫度補(bǔ)償模型。為驗(yàn)證溫度補(bǔ)償模型補(bǔ)償效果及訓(xùn)練效率,對(duì)35℃下傳感器進(jìn)行實(shí)驗(yàn)測(cè)試。結(jié)果表明:35℃下,溫度補(bǔ)償模型補(bǔ)償平均誤差遠(yuǎn)小于單一算法補(bǔ)償效果,驗(yàn)證了此方法具有較高的訓(xùn)練效率及補(bǔ)償效果,能夠提高傳感器在不同溫度、載荷作用下測(cè)量精度,同時(shí)將本文模型應(yīng)用采煤機(jī)截割煤壁工作中,得到導(dǎo)向滑靴在采煤機(jī)行走截割煤壁過(guò)程中受力,為導(dǎo)向滑靴結(jié)構(gòu)優(yōu)化及提高采煤機(jī)可靠性和使用壽命提供依據(jù)。
[Abstract]:In order to solve the problem of temperature drift caused by temperature change in the working process of strain gauge pin shaft sensor, a temperature compensation model of Drosophila algorithm for optimizing RBF neural network is proposed. The expanded parameters of neural network are optimized globally by Drosophila algorithm, and the temperature compensation model is trained by using the measured parameters of stress test platform and the nonlinear mapping ability of neural network. In order to verify the compensation effect and training efficiency of the temperature compensation model, the sensor was tested at 35 鈩，

本文編號(hào)：1984373