Prediction of operational parameters effect on coal flotation using artificial neural network

Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.     

离岸混凝土氯离子扩散系数的人工神经网络模型

为研究人工神经网络在离岸混凝土氯离子渗透中的应用,从已有文献中选用653组数据,建立网络结构为13-27-1的模型进行训练、预测。研究结果表明:人工神经网络能有效预测离岸混凝土中的氯离子扩散系数;水灰比,水泥、减水剂、外加剂(粉煤灰、矿渣、硅灰)、骨料的含量以及混凝土的抗压强度、养护机制、试验方法、暴露时间和暴露环境均会对氯离子扩散系数产生影响。     

Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of fl...     

结构混合控制的神经网络方法

介绍结构混合控制的神经网络方法,以模型结构振动台实验记录为训练样本,训练一个作为控制器使用的多 前向ＢＰ网络来模拟结构的控制准则,并用来模拟未来作动器控制力的大小,训练另一个的神经网络来模拟混合结构控制系统,并用该网络预测结构未来的地震作用下的反应;最后进行数值仿真分析和实验分析的对比研究,结果表明人工神经网络可以在结构混合控制中发挥作用。     

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects, in this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both methods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear relations obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.     

基于人工神经网络的钢筋混凝土异型节点抗剪承载力预测

研究了利用神经网络技术分析钢筋混凝土框架异型节点抗震性能的可行性．从前馈神经网络原理分析出发，利用神经网络方法研究了低周反复荷载作用下的钢筋混凝土框架异型节点抗剪承载力与各主要影响因素之间复杂的非线性关系，并建立了承载力的BP神经网络预测模型．通过与试验结果相对比，新方法获得了令人满意的结果．分析结果表明神经网络计算在钢筋混凝土框架异型节点的抗震行为和力学特性研究领域是一种切实可行且极具发展潜力的新方法．     

Evaluation of nitrate removal effect on groundwater using artificial neural networks

Considering the non-linear,complex and multivariable process of biological denitrification,an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks (ANN) to evaluate the nitrate removal effect. The parameters such as COD,NH3-N,NO-3-N,NO-2-N,MLSS,DO,etc.,were used for input nodes,and COD,NH3-N,NO-3-N,NO-2-N were selected for output nodes. Experimental ANN training results show that ANN was able to predict the output water quality parameters very well. Most of relative errors of NO-3-N and COD were in the range of ±10% and ±5% respectively. The results predicted by ANN model of nitrate removal in groundwater produced good agreement with the experimental data. Though ANN model can optimize effect of the whole system,it cannot replace the water treatment process.     

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.     

应用集成神经网络预测蛋白质相互作用位点

蛋白质相互作用位点在现代药物设计与构建蛋白质相互作用网络方面有着重要的意义。基于一个含有35个蛋白质分子的数据集,首先提取蛋白质的序列谱、熵值、可及表面积3种特征,然后运用误差反向传播神经网络以及其集成对蛋白质的相互作用位点进行了预测。采用35次留一法(一倍交叉验证)进行训练与测试,结果显示每当加入一种新特征时,预测结果都有相应的提高,并且把神经网络集成时,结果又有了一定程度的提高。     

神经网络用于数控系统误差预报补偿技术的研究

为了提高机床数控系统的廓形加工精度，本文通过研究人工神经网络的加工误差预报补偿技术以提高计算机数控系统（ＣＮＣ）的廓形加工精度，并且取得了良好的效果     

基于熵判据自组织模糊神经网络的混沌时间序列预测

在自组织模糊神经网络(SOFNN)算法的基础上提出了一种基于熵判据的改进算法。依据动态自适应方式建立模糊神经网络,采用误差均方根判据和误差熵判据相结合的修剪策略,对网络进行剪裁,去掉对网络输出贡献小的节点。算法的主要优点在于:能够自动地决定神经模型的结构并得出模型的参数,而不需要对神经网络和模糊系统有深入的理论知识,算法具有非常高的预测精度,并且通过修剪策略提高网络的泛化能力。应用该算法对典型的混沌时间序列Mackey-Glass序列进行了研究,结果表明,应用新的修剪策略后,算法精度及泛化能力进一步提高,并且需要的先验知识少,更适合于实际应用。     

Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.     

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situ...     

人工神经网络与统计方法集成预测有机反应产率

在应用人工神经网络预测有机反应产率中,由于结合了统计方法,使人工神经网络易产生的随机性和过拟合作用造成的不利影响减小,从而提高了预测可靠性。     

进化算法结合神经网络反演刚性路面模量

为解决传统路面结构参数反演分析方法易陷于局部最优解的问题,根据弹性地基上的小桡度薄板理论,建立了刚性路面有限元计算模型,模型中假定接缝只传递剪力,用压缩柔度矩阵方法考虑接缝的约束条件,用有限元模型构造训练集对BP神经网络进行训练,依靠BP网络的记忆和联想功能建立结构层模量与路面弯沉盆之间的映射关系,利用进化算法(GA)在搜索空间内找出使弯沉盆误差最小的一组模量组合,实例计算结果表明进化算法结合神经网络方法反演刚性路面模量的最大误差只有2．8％。     

从汶川地震看信息通信网络的容灾性

汶川地震对当地信息通信网络造成了极大的破坏,文章首先提出了完善网络结构,提高网络防御灾害能力的方法,然后介绍了如何建立异地容灾系统,以提高信息网络的容灾性,最后探讨数据容灾系统技术方案。     

基于神经网络的在线英文Pitman速记发声字的识别

介绍了一种对英文Pitman速记发声字进行在线分割识别的新方法，该方法在预处理速记手写体的基础上，采用BP神经网络对发声字分割中可能出现的过分割进行检测和纠正，对音素记号／非音素记号和单个音素记号进行分类和识别，并实现了基于单个笔划识别结果的整体单词识别。通过对68个常用英文单词的测试，验证了该方法的平均识别正确率达到89．6％。     

人工神经网络在改善报警系统性能中的应用

介绍了一种利用BP人工神经网络降低报警系统误报警率的有效方法。以STC89C52RC处理器为平台,在嵌入式系统中应用BP网络先对报警信息进行分析和预处理,再控制报警系统动作。该方法大大降低了报警系统因自然因素导致的误报警概率。采集了100个典型数据对BP网络进行训练,得到了适合应用目标的网络模型,现场实测误报警率几乎为0。该方法的硬件价格便宜,电路简单,是一种降低报警系统误报警率的有效方法。     

神经网络非线性系统自适应控制的研究

提出了一种新的神经网络非线性系统自适应控制方法采用改进的ＢＰ算法，避免了选取学习速率的麻烦仿真结果表明：该方法对非线性系统及突加外干拢、参数突变具有较强的自适应能力     

应用聚类和模糊神经网络设计模糊系统

基于聚类技术和一类模糊神经网络提出一种设计模糊系统的混合方法,通过一个无监督的聚类算法自组织地确定模糊规则的数目及生成一个初始的模糊规则库,构建一类模糊神经网络,通过调整网络的权值,使规则库中的参数更加准确,并以函数逼近问题为例验证了该方法的有效性。