共查询到20条相似文献,搜索用时 718 毫秒
1.
用傅里叶变换红外光谱差减技术自动监测大气毒物的软件及应用 总被引:2,自引:1,他引:2
本文介绍了大气毒物傅里叶变换红外光谱自动差减定量软件,介绍了方法的原理和软件的结构与功能。该技术能够进行数据库的实时咨询,差减谱位的智能搜索和光谱自动差减定量,实现了大气毒物自动定量分析。运用于实际样品分析,快速准确,优于其它的傅里叶变换红外光谱定量技术。 相似文献
2.
采用傅里叶变换红外光谱法测定了航空润滑油中的水分,通过遗传算法(GA)优化选取有效波数点,用误差反向传播神经网络(BP-ANN)进行水分预测计算。模型的预测相关系数为0.957,预测标准偏差为0.022。随机抽取某型航空润滑油样品进行预测并对预测结果进行配对t检验,结果表明:红外光谱定量分析结果与标准方法测定值没有显著性差异,模型可以用于该型在用航空润滑油水分含量现场快速检测。 相似文献
3.
4.
大气中痕量气体污染物的傅里叶变换红外光谱分析 总被引:2,自引:0,他引:2
本文从两种类型的长光程气体红外吸收方法的角度,介绍了傅里叶变换红外光谱在大气痕量气体污染物分析中的应用,并评述了它的现状和发展前景。 相似文献
5.
建立一种基于红外光谱的快速无损地检验洗发用品的分析方法。利用傅里叶红外光谱对60个常见的洗发用品样品进行检验,分别采用Savitzky-Golay(S-G)平滑、快速傅里叶变换(FFT)、降噪等方法对光谱数据进行预处理,并结合主成分分析法对光谱数据进行降维处理。同时建立多层感知器神经网络和贝叶斯判别分析两种分类模型,对光谱数据进行分析验证。多层感知器神经网络对原始数据、经过S-G平滑、FFT、降噪后的分类准确率分别为86.67%、88.33%、80%、90%,贝叶斯判别的分类准确率为83.33%、85%、83.33%、95%。结果显示,降噪处理效果较佳,贝叶斯判别具有更高的准确率。该方法重现性好、样品用量少、无损样品,可为洗发用品类物证鉴定提供科学依据。 相似文献
6.
由于傅里叶变换谱学有许多突出优点,它引起了现代化学家的很大注意。现代谱学中的傅里叶变换核磁共振(FT-NMR),傅里叶变换红外光谱(FT-IR),傅里叶变换质谱(FT-MS)等技术国内外已有许多文献介绍。最近电化学也出现了快速傅里叶变换电化学弛豫测定法(FFT-ERM),它对于研究电化学动 相似文献
7.
主成分-线性判别法对大气易挥发性有机化合物的预警 总被引:1,自引:0,他引:1
应用遥感傅里叶变换红外光谱,采用主成分提取-线性判别分析(PCA-LDA)技术,对丙酮、二氯甲烷、甲苯、苯、氯仿和甲醇等六组分的任意混合体系进行定性鉴别。被选用的这6种大气有毒有机化合物的红外光谱图相互间存在着严重的混叠,并和反向传播人工神经网络(BP-ANN)的预测结果进行了比较。PCA-LDA的鉴别判对率达92.2%,识别率94.4%,误判率7.8%;BP-ANN分别为91.1%、95.6%和8.9%。结果表明PCA处理克服了LDA对多变量数据预测的局限性,预测性能和BP-ANN相当。鉴于BP-ANN计算耗时和繁琐,PCA-LDA模型被确定为建立VOCs预警模型最适当的方法。 相似文献
8.
9.
应用小波和小波包变换对傅里叶变换衰减全反射红外光谱(FTIR/ATR)进行去噪处理,以提高苯丙酮尿症(PKU)筛查模型的性能。首先优化小波和小波包变换的参数,然后分别对原始光谱(OS)、9点平滑光谱(9S)和一阶微分9点平滑光谱(1D9S)进行去噪处理,以均方根误差(RMSE)、平均相对误差(MRE)、预测准确率(Acc)等为指标,考察小波和小波包变换对模型性能的影响。结果与变换前相比,模型性能均有所提高,其中小波变换以1D9S+sym12处理结果为最优,而小波包变换以1D9S+sym1为最优;Acc全部提高为100%。 相似文献
10.
《化学研究与应用》2018,(12)
改性沥青中SBS的含量是评价改性沥青的重要指标。本研究中,采用溴化钾压片溶膜技术的傅里叶变换红外光谱法结合人工神经元网络方法对改性沥青中SBS含量进行定量分析。使用四个梯度SBS含量的改性沥青样品作为训练集、两个特定SBS含量的改性沥青样品作为测试集,通过使用溴化钾压片溶膜技术的傅里叶变换红外光谱测试采集其红外光谱数据,并使用人工神经元网络方法对其红外光谱数据进行处理和分析。结果显示,经过数据预处理和人工神经元网络方法,可以对改性沥青中SBS进行定量分析,其结果误差小、准确度高。因此,使用傅里叶变换红外光谱结合人工神经元网络方法可以快速、便捷、准确的对改性沥青中SBS的含量进行测量。 相似文献
11.
In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis. 相似文献
12.
Lloyd GR Brereton RG Faria R Duncan JC 《Journal of chemical information and modeling》2007,47(4):1553-1563
Learning vector quantization (LVQ) is described, with both the LVQ1 and LVQ3 algorithms detailed. This approach involves finding boundaries between classes based on codebook vectors that are created for each class using an iterative neural network. LVQ has an advantage over traditional boundary methods such as support vector machines in the ability to model many classes simultaneously. The performance of the algorithm is tested on a data set of the thermal properties of 293 commercial polymers, grouped into nine classes: each class in turn consists of several grades. The method is compared to the Mahalanobis distance method, which can also be applied to a multiclass problem. Validation of the classification ability is via iterative splits of the data into test and training sets. For the data in this paper, LVQ is shown to perform better than the Mahalanobis distance as the latter method performs best when data are distributed in an ellipsoidal manner, while LVQ makes no such assumption and is primarily used to find boundaries. Confusion matrices are obtained of the misclassification of polymer grades and can be interpreted in terms of the chemical similarity of samples. 相似文献
13.
14.
15.
A quantitative fuzzy neural network (Q-FNN) for pattern recognition in analytical determination is reported in this paper. The fuzzy neural network (FNN) combines a fuzzy logic system with an artificial neural network (ANN) so that it has both advantages of a high training speed and strong anti-interference. Importantly, the analytical concept of relative error (RE) in quantitative determination has been integrated into FNN so that the Q-FNN provides a very good quantitative capability in chemical analysis, and prevents the system from an over-fitting problem. The logarithm curve with noise in terms of analytical response versus concentration is calibrated by trained FNN and a close approximation to the ideal one without noise is obtained. The Q-FNN has been applied to the concentration determination of freon in the presence of interference gases. The prediction error for a test set in quantification is less than 10% while no qualitative mistake is observed, implying that the quantitative FNN has sustained the feature of pattern recognition. The results indicate that the Q-FNN has obvious advantages not only in converging speed, but also in the quantitative accuracy over the ANN. 相似文献
16.
17.
本文提出一种基于RBF(Radial Basis Function,径向基函数)神经网络的打印机光谱预测模型,通过扩展神经网络模型输入变量的项数提高模型的预测精度,扩展项多采用通道驱动值的交叉值、平方值.实验结果表明[1cmy]项的引入能够有效提高模型的预测精度,同时提高网络的泛化能力.而引入[cm2 cy2 mc2 my2 yc2 ym2]项会导致模型预测精度以及泛化能力降低.[1 cmy]、[c2m2y2]和[cm cy my]项的组合在预测精度和模型泛化能力上均是最优化的,对总样本预测的色度精度为0.475ΔE00,光谱精度RMSE为0.43%.因此选择[1 cmy c2m2y2 cm cy my c m y]作为输入变量的RBF神经网络训练模型是满足高精度光谱预测的最优模型. 相似文献
18.
19.
A feed-forward neural network has been developed to predict the solvent accessibility/accessible surface area (ASA) of proteins using improved design and training methods. Several network issues ranging from the coding of ASA states to the problem of local minima of learning curve, have been addressed. Successful new approaches to overcome these problems are presented. Set of trained network weights for each ASA threshold is provided. It has been established that the prediction accuracy results with neural network are better than other reported results of ASA prediction, despite a high test to training data ratio. 相似文献