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1.
根据汽油辛值预测体系本身的非线性特点,提出主成分回归残差神经网络校正算法(principal component regression residual artificial neural network,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正,该方法给合了主成分回归算法(PC),与经典的线性校正算法(PLS(Partial Least Square),PCR, 以及非线性PLS(NPLS,Non-linear PLS)等相比,预测明显的改善,文中还讨论了PCR主成分数及训练参数对预则模可能的影响。  相似文献   

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对多层前传网络的过拟合问题进行了探讨。定义了逼近误差和逼近度作为人工神经网络(ANN)的建模评价指标。通过应用于多元非线性校正的数值模拟和实际药物光度分析数据解析,表明该指标意义明确,便于掌握,且能较好地定量表述ANN逼近规律的程度。  相似文献   

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对多层前传网络的过拟合问题进行了探讨,定义了逼近误差和逼近度作为人工神经网络(ANN)的建模评价指标,通过应用于多元非线性校正的数值模拟和实际药物光度分析数据解析,表明该指示明确,便于掌握,且能较好的地定量表述ANN逼近规律的程度。  相似文献   

4.
神经网络法在使用裂解气相色谱鉴别中草药中的应用   总被引:10,自引:0,他引:10  
将以误差反向传播为训练算法的前馈式人工神经网络(BP-ANN)首次艇于中草药的裂解气相色谱谱图解析。重点考察了如何表征和提取复杂的裂解色谱图中有价值信息,用于主成分分析方法处理后输入到有数经优化的神经网络中。实验证明,该广阔示仅可以正确识别样品所属种类,耐用对一示同实验时间、数据残缺等原因造成的噪音具有优异的抗干扰能力。  相似文献   

5.
催化动力学光度法同时测定铜和银   总被引:14,自引:0,他引:14  
基于Cu(Ⅱ)、Ag(Ⅰ)在硼酸-氢氧化钠缓冲介质中,能同时催化过硫酸铵氧化邻甲氧基酚的显色反应,实现了Cu(Ⅱ)、Ag(Ⅰ)的催化动力学分析法同时测定,研究了最佳反应条件,用于合成样品测定时比较了多元线笥回归(MLR)、卡尔紧滤波(KF)和人工神经网络(ANN)用于数据处理的优劣,发现以ANN法的结果最好。此法用于铅锌矿废渣中铜、银的同时测定,结果满意。  相似文献   

6.
人工神经网络—紫外光谱定量多组分体系的研究   总被引:7,自引:2,他引:5  
潘忠孝  王拴虎 《分析化学》1994,22(9):939-941
本文系统地考察了人工神经网络(ANN)-紫外光谱(UVS)同时定量多组分混合溶液时参数选择时网络训练和预报性能的影响。合理选取诸参数,可提高训练效率改善预报性能,而且所优化的参数集可移植到其它相似体系。  相似文献   

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人工神经网络法用于高效毛细管电泳分离条件优化的研究   总被引:9,自引:1,他引:8  
把人工神经网络(ANN)法应用于高效毛细管电泳(HPCE)分离条件的优化,给出了反向传播(BP)的ANN模型的具体算法。用正交试验法同时考察了缓冲溶液组成、浓度、pH值和有机添加剂浓度等实验因素对HPCE分离合成色素和防腐剂的影响,采用误差反向传播方法建立了有效的ANN预测模型,预测最佳分离条件,获得了满意的分离结果。  相似文献   

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模式识别用于压电晶体传感器阵列识别可燃物质   总被引:4,自引:0,他引:4  
用7个压电晶体组成传感器阵列,每个晶体上分别涂有不同种类的气相色谱固定液,通过测定各种可燃物质燃烧时放出的混合气体来识别所燃物质,在识别中分别应用了人工神经网络法(ANN)和逐步判别分析法(SDA)。讨论了解决神经网络开始训练时不收敛或产生麻痹现象的方法,提出了训练数据选取的新方法-训练集逐步扩展法,实验证明:人工神经网络对被测物质的识别准确率达100%,高于逐步判别分析法(83%)。  相似文献   

9.
人工神经网络-伏安分析法同时测定邻、间、对二硝基苯   总被引:3,自引:0,他引:3  
将反向传播算法的前馈神经网络用于导数脉冲伏安分析法同时测定邻、间、对二硝基苯。实验在盐酸-氯化钾-乙醇介质中进行,悬汞电极作为工作电极。通过对网络结构和参数的优化,加快了训练速度,提高了预测的准确度。用该法对邻、间、对二硝基苯混合物进行定量分析,预测的相对标准误差(SEP)分别为426%,499%和486%。对人工神经网络(ANN)和偏最小二乘法(PLS)的结果进行的比较表明,ANN法优于PLS法。  相似文献   

10.
人工神经网络及其在色谱中的应用   总被引:4,自引:0,他引:4  
本文对人工神经网络(ANN)在色谱保留机理的研究和保留值的预测,溶质分子构型与色谱保留的定量关系(QSRR),谱图解析等方面的应用做了综述。着重介绍了BP网络的发展及其在色谱领域中的应用,并对ANN在色谱中的应用前景做了预测。  相似文献   

11.
Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.  相似文献   

12.
Simultaneous determination of several elements (U, Ta, Mn, Zr and W) with inductively coupled plasma atomic emission spectrometry (ICP-AES) in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network (ANN) and partial least squares regression (PLS) for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line (263.553 nm) with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 (input, hidden and output neurons) network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions (REP) in test set were 3.75% and 3.56% for ANN and PLS respectively.  相似文献   

13.
A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.  相似文献   

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Zvi Boger   《Analytica chimica acta》2003,490(1-2):31-40
Instrumentation spectra used for chemometrics analysis are often too unwieldy to model, as many of the inputs do not contain important information. Several mathematical methods are used for reducing the number of inputs to the significant ones only. Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs. However, using a non-random initial connection weight algorithm and local minima avoidance and escape techniques can overcome these difficulties. Once the ANN model is trained, the analysis of its connection weights can easily identify the more relevant inputs. Repeating the process of training the ANN model with the reduced input set and the selection of the more relevant inputs can proceed until a quasi-optimal, small, set of inputs is identified. Two examples are presented—finding the minimal set of wavelengths in benchmark diesel fuel NIR spectra, and in spectra generated in a recent work, modeling of “artificial nose” sensor array. In the last example, 1260 inputs were reduced to optimal sets of <10 inputs. Causal index calculation can analyze the influence of each of selected wavelengths on the predicted property. Some of the resulting minimal sets are not unique, depending on the ANN architecture used in the training. The accuracy of the resulting ANN models is usually better, and more robust, than the original large ANN model.  相似文献   

17.
人工神经网络在纸浆卡伯值光学定量分析中的应用   总被引:2,自引:0,他引:2  
卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460~ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…  相似文献   

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