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基于偏最小二乘与广义回归神经网络的近红外光谱测定土豆中3种营养成分的研究 总被引:1,自引:0,他引:1
偏最小二乘(partial least squares,PLS)与广义回归神经网络(generalized regression neural networks,GRNN)联用对土豆样品建立起粗纤维、淀粉、蛋白质含量的预测校正模型,用PLS法将原始数据压缩为主成份,取前3个主成份的12个特征吸收峰输入GRNN网络,网络光滑因子σi为0.1.PLS-GRNN模型对样品3个组分含量的预测决定系数(R2)分别为: 0.945、 0.992、 0.938.结果表明,近红外光谱技术可以快速、准确地同时测定土豆中的粗纤维、淀粉、蛋白质,该方法可应用于果蔬产业的品质管理与控制. 相似文献
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以连钱草的毛细管电泳指纹图谱为输入数据,以总黄酮和三萜酸类成分含量为输出数据,构建了反向传播网络、径向基函数网络和广义回归网络三种人工神经网络模型.采用三种网络模型和两种预测方法对未知样本的总黄酮和三萜酸类成分含量进行了预测,并分别比较了三种网络和两种预测方法的预测结果.另外,结合聚类分析结果和输入数据的相似度,分析了预测误差的来源.结果表明:三种网络对大部分样本的预测值与实际值都比较接近,而广义回归网络的预测效果最优;扣除奇异值后,广义回归网络的两种预测方法对未知样本的总黄酮和三萜酸类成分含量的平均预测误差分别为10.9%和0.00073%. 相似文献
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溶解度作为一项重要的物化指标,一直是化学学科的研究重点。然而,通过实验测量获得数据耗时费力,因此,科研人员建立了多种理论方法来进行估算,其中,人工神经网络因其能够关联复杂的多变量情况而受到广泛关注。本文综述了人工神经网络在物质溶解度预测方面的应用,介绍了应用最广泛的3种神经网络(BP神经网络、小波神经网络、径向基神经网络)的模型结构、预测方法和预测优势,探讨了神经网络的不足以及改进方法。文章最后对神经网络在物质溶解度预测方面的发展前景进行了展望。与其他方法相比,人工神经网络技术在物质溶解度预测方面具有预测结果精确度高、操作简单等特点,具有广阔的应用前景,但输入变量选择、隐含层节点数确定、避免局部最优等问题还需逐步建立系统的理论指导。 相似文献
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反弹传播神经网络用于痕量铬的示波计时电位法测定 总被引:6,自引:0,他引:6
首次将反弹传播算法神经网络用于铜箔钝化液中痕量铬的示波计时电位法测定。探讨了网络层数、层结点数和结点转移函数等网络参数对预测结果的影响。实验结果表明 :Cr 浓度在 4.0× 10 -7~ 1.3× 10 -6mol/L范围内与示波计时电位曲线上的切口深度呈线性关系 ,检测下限可达 8× 10 -8mol/L ;与标准BP神经网络的训练和预测结果相比较 ,反弹传播神经网络用于示波测定时不仅具有较高的预测精度 ,而且大大提高了网络训练的收敛速度 相似文献
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利用高光谱技术对培养基上细菌(大肠杆菌、李斯特菌和金黄色葡萄球菌)菌落进行快速识别和分类。采集琼脂培养基上细菌菌落的高光谱反射图像(390~1040 nm),在对波段差图像进行大津阈值分割的基础上自动提取细菌菌落光谱,并建立细菌分类检测的全波长和简化偏最小二乘判别( PLS-DA)模型。全波长模型对预测集样本的分类准确率和置信预测分类准确率分别为100%和95.9%。此外,利用竞争性自适应重加权算法( CARS)、遗传算法( GA)和最小角回归算法( LARS-Lasso)进行波长优选并建立对应简化模型。其中,CARS简化模型在精度、稳定性及分类准确率方面均优于GA和LARS-Lasso简化模型,其对预测集样本的分类准确率和置信预测分类准确率分别达到了100%和98.0%。研究表明,高光谱是一种细菌菌落高精度、快速、无损识别检测的有效方法。简化模型中优选的波长可以为开发低成本检测仪器提供理论依据。 相似文献
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Synthesis of carbon nanotube‐based nanocomposite and application for wastewater treatment by ultrasonicated adsorption process 下载免费PDF全文
The sorption of methylene blue (MB) and basic yellow 28 (BY28) dyes in water on Ag@ZnO/MWCNT (Ag‐doped ZnO loaded on multiwall carbon nanotubes) nanocomposite is investigated in a batch process, optimizing starting initial dye concentration, sonication time and adsorbent mass. Isotherms and kinetic behaviours of MB and BY28 adsorption onto Ag@ZnO/MWCNT were explained by extended Freundlich and pseudo‐second‐order kinetic models. Ag@ZnO/MWCNT was synthesized and characterized using X‐ray diffraction, energy‐dispersive X‐ray spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller analysis. According to the experimental data, adaptive neuro‐fuzzy inference system (ANFIS), generalized regression neural network (GRNN), backpropagation neural network (BPNN), radial basic function neural network (RBFNN) and response surface methodology (RSM) were developed, and applied to forecast the removal performance of the sorbent. The influence of process variables (i.e. sonication time, initial dye concentration, adsorbent mass) on the removal of MB and BY28 was considered by central composite rotatable design of RSM, GRNN, ANFIS, BPNN and RBFNN. The performances of the developed ANFIS, GRNN, BPNN and RBFNN models were compared with RSM mathematical models in terms of the root mean square error, coefficient of determination, absolute average deviation and mean absolute error. The coefficients of determination calculated from the validation data for ANFIS, GRNN, BPNN, RBFNN and RSM models were 0.9999, 0.9997, 0.9883, 0.9898 and 0.9608 for MB and 0.9997, 0.9990, 0.9859, 0.9895 and 0.9593 for BY28 dye, respectively. The ANFIS model was found to be more precise compared to the other models. However, the GRNN method is much easier than the ANFIS method and needs less time for analysis. So, it has potential in chemometrics and it is feasible that the GRNN algorithm could be applied to model real systems. The monolayer adsorption capacity of MB and BY28 was 292.20 and 287.02 mg g?1, respectively. 相似文献
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本文采用小波潜变量回归(WLVR)方法,同时测定重叠的光谱信号。结合小波阈值法和主组分分析(PCA)改进除噪质量。八个误差判据用于推断因子数目。潜变量由小波处理过的信号投影到正交基矢量而获得。广义回归神经网络(GRNN)被应用于多组分同时测定。依据算法原理编制了三个程序(PWMRA、PWLVR和PGRNN)执行有关计算。三个方法(WLVR、LVR(潜变量回归)和GRNN)同时测定三组分混合物,获得满意的结果。 相似文献
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Baskin II Ait AO Halberstam NM Palyulin VA Zefirov NS 《SAR and QSAR in environmental research》2002,13(1):35-41
An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes. 相似文献
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I.I. Baskin A.O. Ait N.M. Halberstam V.A. Palyulin N.S. Zefirov 《SAR and QSAR in environmental research》2013,24(1):35-41
An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes. 相似文献