遗传算法-贝叶斯正则化BP神经网络拟合滴定糖蜜中有机酸

分别用常规BP神经网络、贝叶斯正则化BP神经网络及遗传算法-贝叶斯正则化BP神经网络,对多组分有机酸的滴定数据进行主成分非线性拟合.结果显示,贝叶斯正则化能限制网络权值,避免过拟合;遗传算法则使网络的全局优化能力和稳健性提高.对26个测试样本中的乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸6种组分,以及柠檬酸和乌头酸的总量...     

Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network

Coal ash fusion temperature is important to boiler designers and operators of power plants. Fusion temperature is determined by the chemical composition of coal ash, however, their relationships are not precisely known. A novel neural network, ACO-BP neural network, is used to model coal ash fusion temperature based on its chemical composition. Ant colony optimization (ACO) is an ecological system algorithm, which draws its inspiration from the foraging behavior of real ants. A three-layer network is designed with 10 hidden nodes. The oxide contents consist of the inputs of the network and the fusion temperature is the output. Data on 80 typical Chinese coal ash samples were used for training and testing. Results show that ACO-BP neural network can obtain better performance compared with empirical formulas and BP neural network. The well-trained neural network can be used as a useful tool to predict coal ash fusion temperature according to the oxide contents of the coal ash.     

神经网络Kalman滤波算法及多组分光度分析应用

前馈神经网络ＮＮ误差反向传播算法（ＢＰ）收敛速度较慢且常陷入局部极优值等，针对此种缺陷提出了一种基于扩展Ｋａｌｍａｎ滤波的快速学习新算法（ＥＦ）。与ＢＰ相比，ＥＦ法不仅学习效率高收敛速度快，数值稳定性好，而且所需学习次数少，调节参数少，由非线性系统建模与辨识的模拟结果表明，ＥＦ是提高网络收敛速度改善神经学习性能的一种有效方法，谈谈用于多组分光谱分析，结果良好。     

Simultaneous quantification of analytes in quaternary mixtures using dual pulse staircase voltammetry and genetically optimised neural networks

This paper describes the simultaneous quantification of four aliphatic compounds (ethanol, methanol, fructose and glucose) mixed in varying concentrations. The method used employs dual pulse staircase voltammetry (DPSV) to acquire an electrochemical voltammogram from the mixture. An artificial neural network, optimised using an elitist genetic algorithm, is then used to determine the concentration of each individual analyte from the voltammogram. The best average RMS errors achieved when testing with unseen data were 5.03%, 7.72%, 3.29% and 4.00% for maximum analyte concentrations of ethanol, methanol, fructose and glucose respectively. This work represents an important step forward because DPSV data is notoriously difficult to calibrate due to complex electrode-analyte interactions, and had not previous been shown to be amenable to quaternary mixtures.     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

人工神经网络预测煤炭成浆浓度的研究

考虑煤炭的多种理化特性建立了成浆浓度的神经网络预测模型，对其数据预处理方法、学习率和中间层节点数等进行了深入讨论。水分、挥发分、分析基碳、灰分和氧等五个因子对于煤炭成浆性的预测起到主导作用。五因子、七因子和八因子神经网络模型对煤炭成浆浓度的预测误差分别为：0.53%、0.50%和0.74%，而现有回归分析方程的误差为1.15%，故神经网络模型比回归分析方程有更好的预测能力，尤以七因子模型最佳。     

基于分步相关成分分析的中药材质量鉴别神经元分类器

提出并构建了一种基于分步相关成分分析的神经元分类器(SCCA-HBP),并将其用于中药材质量模式分类.通过从色谱分析所得到的高维数据集中分步提取分类相关成分,获取化学模式特征向量,使神经元分类器输入模式向量的维数降低.此外,提出用带输出误差死区的混合BP算法训练神经元分类器,提高了网络学习训练速度和分类准确性.以32个当归样品质量等级分类鉴别为例考察本方法,分类正确率为100%,优于PCA-BP(84.4%)和SCCA-BP(90.6%)方法;且训练时间仅为BP算法的54.2%.     

Monitoring storage time and quality attribute of egg based on electronic nose

The objective of this study was to investigate the potential of an electronic nose (E-nose) technique for monitoring egg storage time and quality attributes. An electronic nose was used to distinguish eggs under cool and room-temperature storage by means of principal component analysis (PCA), linear discriminant analysis (LDA), BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose could distinguish eggs of different storage time under cool and room-temperature storage by LDA, PCA, BPNN and GANN; better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices (Haugh unit and yolk factor) by quadratic polynomial step regression (QPSR). The prediction models for Haugh unit and yolk factor indicated a good prediction performance. The Haugh unit model had a standard error of prediction of 3.74 and correlation coefficient 0.91; the yolk factor model had a 0.02 SEP and 0.93 correlation coefficient between predicted and measured values respectively.     

LM优化反向传播网络测定多组分

为了提高此网络算法的学习效率及稳定性,在反向传播算法（backpropagation(BP)中引入了基于非线性最小二乘法的Levenberg-Marquart(LM)最优算法,替代原BP算法中的梯度下降法寻找最佳网络连接权值,LM优化算法其学习效率比带动量项的BP算法高一个数量级以上,值得推广应用,将其用于混合体系的多组份CAS－CTMAB显色体系光度法同时测定Ca,Mg,Fe,得到平均预测误差为2．6534mg/L,平均预测方差为1．9580,能够满足多组分测定的需要。     

小波神经网络-遗传算法用于2-(9-咔唑)乙基氯甲酸酯衍生化氨基酸的胶束电动力学色谱分离优化

将小波神经网络和遗传算法应用到2-(9-咔唑)-乙基氯甲酸酯衍生化氨基酸的胶束电动力学色谱分离优化。小波神经网络结合正交试验设计用于分离过程的多因素模型建立。以训练好的小波神经网络模型为目标函数,采用实数编码的遗传算法搜寻确定最佳分离条件,在此条件下分离得到的归一化分离度积与正交试验设计中最佳条件相比,提高了12．5％。     

VOCs分子的半导体型传感器识别检测研究进展

具有体积小、功耗低、灵敏度高、硅工艺兼容性好等优点的金属氧化物半导体(MOS)气体传感器现已广泛地应用于军事、科研和国民经济的各个领域。然而MOS传感器的低选择性阻碍了其在物联网(IoT)时代的应用前景。为此,本文综述了解决MOS传感器选择性的研究进展,主要介绍了敏感材料性能提升、电子鼻和热调制三种改善MOS传感器选择性的技术方法,阐述了三种方法目前所存在的问题及其未来的发展趋势。同时,本文还对比介绍了机器嗅觉领域主流的主成分分析(PCA)、线性判别分析(LDA)和神经网络(NN)模式识别/机器学习算法。最后,本综述展望了具有数据降维、特征提取和鲁棒性识别分类性能的卷积神经网络(CNN)深度学习算法在气体识别领域的应用前景。基于敏感材料性能的提升、多种调制手段与阵列技术的结合以及人工智能(AI)领域深度学习算法的最新进展,将会极大地增强非选择性MOS传感器的挥发性有机化合物(VOCs)分子识别能力。     

超声衰减谱测量电池浆料的粒度分布

电池浆料中颗粒状活性物质的粒度大小和分散均匀性对电池的内阻、 电压、 局部表面电流和总极化程度等性能有直接影响, 实现对其的在线实时测量对电池的质量控制具有重要意义. 基于电池浆料的高固含量、 高黏度和低透光性的特点, 本文利用超声衰减谱的方式测量了其粒度分布(PSD). 应用于电池浆料的粒度分布测量的最大难点是其利用超声衰减谱法预测粒度分布的模型需要难以获得的分散相和连续相的物性参数. 本文采用主成分分析(PCA)结合误差反向传播(BP)神经网络建立预测模型解决了超声衰减谱法的难点, 并引入遗传算法(GA)优化BP神经网络的初始阈值和权值. 通过以LiCoO₂为活性物质的电池浆料进行了验证, 结果表明, PCA-GA-BP神经网络能够有效对不同固含量电池浆料的粒度分布进行预测, 预测值与真实值的峰形重合度高, 峰高偏差小, 两者的均方误差为0.1358, 拟合度(R²)为0.9816, 说明超声衰减谱法可作为测量电池浆料粒度分布的重要方式.     

Classification and identification of mosquito species using artificial neural networks

An artificial neural network method is presented for classification and identification of Anopheles mosquito species based on the internal transcribed spacer2 (ITS2) data of ribosomal DNA string. The method is implemented in two different multi-layered feed-forward neural network model forms, namely, multi-input single-output neural network (MISONN) and multi-input multi-output neural network (MIMONN). A number of data sequences in varying sizes of different Anopheline malarial vectors and their corresponding species coding are employed to develop the neural network models. The classification efficiency of the network models for untrained data sequences is evaluated in terms of quantitative performance criteria. The results demonstrate the efficiency of the neural network models to extract the genetic information in ITS2 sequences and to adapt to new data. The method of MISONN is found to exhibit superior performance over MIMONN in distinguishing and identification of the mosquito vectors.     

Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results.