本文選題：黃河含沙量　切入點(diǎn)：卡爾曼濾波　出處：《應(yīng)用基礎(chǔ)與工程科學(xué)學(xué)報(bào)》2016年05期 　論文類型：期刊論文

【摘要】：針對(duì)黃河含沙量測(cè)量易受環(huán)境因素影響而導(dǎo)致測(cè)量結(jié)果不準(zhǔn)確的問(wèn)題,提出基于卡爾曼和BP神經(jīng)網(wǎng)絡(luò)(Kalman-BP)的協(xié)同融合模型,將含沙量、水溫和流速等傳感器輸出值經(jīng)過(guò)卡爾曼濾波器進(jìn)行濾波處理;然后經(jīng)BP神經(jīng)網(wǎng)絡(luò)模型對(duì)含沙量信息和環(huán)境量信息進(jìn)行多傳感器數(shù)據(jù)融合;最后建立了含沙量測(cè)量的反演模型.為了比較Kalman-BP神經(jīng)網(wǎng)絡(luò)的協(xié)同處理方法的融合效果,在相同環(huán)境下還進(jìn)行了一元線性回歸模型和多元線性回歸模型的含沙量數(shù)據(jù)處理,并進(jìn)行了誤差分析比較.實(shí)驗(yàn)結(jié)果表明,Kalman-BP神經(jīng)網(wǎng)絡(luò)協(xié)同融合模型的測(cè)量誤差較小,提高了含沙量測(cè)量系統(tǒng)的精度.
[Abstract]:In order to solve the problem that the measurement of sediment content in the Yellow River is easily affected by environmental factors, a cooperative fusion model based on Kalman and BP neural network Kalman-BP) is proposed. The output values of the sensor such as water temperature and velocity are filtered by Kalman filter, and then the information of sediment content and environment are fused by BP neural network model. Finally, the inversion model of sediment content measurement is established. In order to compare the fusion effect of the cooperative processing method of Kalman-BP neural network, the data processing of single linear regression model and multivariate linear regression model is carried out in the same environment. The experimental results show that the measurement error of Kalman-BP neural network cooperative fusion model is small and the precision of sediment content measurement system is improved.
【作者單位】： 華北水利水電大學(xué)信息工程學(xué)院;鄭州大學(xué)水利與環(huán)境學(xué)院;
【基金】：國(guó)家科技重大專項(xiàng)(2014ZX03005001) 水利部黃河泥沙重點(diǎn)實(shí)驗(yàn)室開(kāi)放課題基金項(xiàng)目(2012005) 河南省高�？萍紕�(chuàng)新團(tuán)隊(duì)支持計(jì)劃(13IRTSTHN023) 河南省高等學(xué)校重點(diǎn)科研項(xiàng)目計(jì)劃(14B170012,15A510003)
【分類號(hào)】：TV149.1
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本文編號(hào)：1574092