人工神经网络用于光度法测定工业废水中的铬和铁

为了快速测定工业废水中的铬和铁,利用BP人工神经网络模型,并与分光光度法相结合,不经分离同时测定工业废水中的铬和铁。在铬-铁-二苯碳酰二肼显色体系中,控制pH在1.0-2.0之间,用分光光度法测定显色体系的吸收光谱,应用三层人工神经网络解析吸收光谱,同时测得铬和铁的浓度。详细研究了分光光度法同时测定铬和铁的测定条件和网络训练的最佳训练参数,BP人工神经网络的动量参数为0.8,拓扑结构为20-15-2,转换函数的形状参数为0.9。该测定方法不仅可用于环境监测,而且能用于食品、材料、药物、生物样品、矿物等物质中铬和铁的测定。     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

The reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%). Copyright © 2008 John Wiley & Sons, Ltd.     

应用人工神经网络预测多氯代二苯骈呋喃的色谱保留指数

以量子化学方法在密度泛函B3LYP/6-31G(d)水平上计算得到了多氯代二苯骈呋喃系列化合物(PCDF)分子的结构参数:最高占据轨道能(EHOMO)、最低空轨道能(ELUMO)、最正原子净电荷(q+)、最负原子净电荷(q-)、分子偶极矩(μ)、极化率(α)、分子平均体积(Vm)、恒容热容(C■V).采用误差反向传播(BP)算法的人工神经网络,建立了EHOMO、ELUMO、q+、q-、μ、α、Vm、C■V与PCDF色谱保留指数之间关系的模型,检测样本的预报值与实验值相对误差范围为-1.66%².39%,平均相对误差为0.31%,达到了很好的预测效果.     

Predicting nucleic acid torsion angle values using artificial neural networks

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles , and from torsion angles , , and for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for , , and are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.     

Esters flash point prediction using artificial neural networks

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4‐5‐8‐5‐1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R²) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set). © 2012 Wiley Periodicals, Inc.     

Quantitative prediction of substituted products based on quantum-chemical parameters and neural network method

The criterion of orientating group of electrophilic aromatic nitration was discussed by means of pattern recognition method with quantum-chemical parameters as features, and the product ratios of the reactions were quantitatively calculated using artificial neural network (ANN) method with the same parameters as inputs, based on the ab initio calculation of quantum chemistry. The quantum-chemical parameters involved orbital energy, orbital electron population, atomic total electron density and atomic net charge. The predicted values are in agreement with experimental results and (he predicted error of the ANN with quantum-chemical parameters for the reaction is the smallest among the all methods.     

学习向量量化神经网络用于胃癌组织样品分类识别的研究

将lvq神经网络(Learn ing Vector Quantization Neural Networks)用于胃癌组织样品的分类识别,根据胃癌组织及相应正常组织的FTIR光谱的主要特征吸收峰值(包括vas(CH3)、vs(CH2)、δ(CH2)、v(C-O)、vs(PO2-)、vas(PO2-)和vs(核酸,细胞蛋白及膜脂))全部或部分作为网络输入向量,对未知的胃组织样品进行分类识别,结果显示:i)以上述全部七个谱峰为输入向量时,网络经训练学习后,其平均识别正确率最高(达89.3%),表明该网络对胃癌组织样品的分类识别是满意的,完全可作为临床医学的辅助诊断手段;ii)总体上,当作为输入向量的FTIR特征谱峰越多时,则网络的平均分类识别正确率越高;iii)作为输入的FTIR特征谱峰不同时,则网络的平均分类识别正确率也不同。     

人工神经网络法同时测定苯二酚异构体

研究了人工神经网络法与紫外分光光度分析相结合，用于邻苯二酚、间苯二酚、对苯二酚异构体的含量测定。本研究采用人工神经网络法，直接对苯二酚异构体混合液的紫外吸收光谱数据进行预测，不需预先分离，即可得到各异构体的浓度。     

Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result.     

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.     

Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.     

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.     

Analysis of ultrasonic velocity in refrigerants using artificial neural network

Several researchers have reported numerous measurements on ultrasonic velocity as a function of temperature and pressure using various experimental techniques. A large amount of experimental data is required in order to obtain accurate results for the chemical substances used. The present article explores the evaluation of ultrasonic velocity as a function of molecular weight, temperature and pressure using an artificial neural network (ANN) in six refrigerants. The network so developed predicts the ultrasonic velocity successfully. Statistical analysis of the results was performed using standard deviation (%) and relative average deviation. The correlation coefficient in our analysis was found to be 0.9999. The trained weights, obtained from ANN, are further employed to form equations to predict ultrasonic velocity at other temperatures and pressures.     

Application of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts

Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 μm. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R²) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method.