支持向量机在混炼胶炭黑分散度预测中的应用

以最大瞬时功率、混炼时间、混炼温度和单位能耗作为输入变量,采用支持向量机建立混炼胶炭黑分散度的预测模型,以实现密炼机混炼胶分散度的在线预测.通过在5种配方胶料的各13个样本中取44个组成训练集,并在每种配方胶料中抽取4个进行测试,对支持向量机模型进行训练和测试.结果表明,采用支持向量机对混炼胶分散度进行预测是可行的.     

支持向量机在炸药爆热预测中的应用

根据支持向量机(SVM)理论,基于支持向量回归机(SVR)原理。利用Matlab语言,设计炸药爆热预测模型,通过已知炸药爆热预测,对模型进行验证,并对另外几个炸药进行预测。结果表明,SVR模型对爆热的预测可以得到较好的预测结果,运行速度较快,精度较高,具有良好的应用前景,可为爆热预测提供理论依据。     

最小二乘支持向量机在水平井产能预测中的应用

随着油气勘探开发技术的发展,水平井以其泄油面积大、油气流动阻力小的突出优点越来越受到人们的重视。通过对水平井的产能预测,可以预测该水平井的开发效益及经济回报,因此对水平井的产能研究变得尤为重要。鉴于水平井产能影响因素的复杂性,本文运用最小二乘支持向量机技术,对多因素非线性影响下的水平井产能进行预测,并通过实例说明该方法的实用性。     

基于支持向量机回归的天然气负荷预测研究

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.     

应用ANFIS预测混炼胶粘度

混炼胶粘度是影响混炼胶后序加工过程的员重要的因素之一，同时也是影响橡胶制品质量的重要因素，因此，根据混炼胶的粘度来控制泥炼过程是非常必要的。但由于混炼胶的粘度在线测量难度大，通过建立有效的预测模型，采用计算的方法来预测混炼胶的粘度是一种行之有效的方法。首次探讨了通过应用自适应模糊推理系统(ANFIS)来建立混炼胶粘度的预测模型，并根据已有的实验数据预测了混炼胶粘度，达到了较高的预测精度。     

支持向量机在粉煤灰分类中的应用

针对以往粉煤灰分类的片面性,选择粉煤灰有关的化学成分和物理性质为特征向量,提出了基于支持向量机的粉煤灰科学分类。结果表明,此方法不仅结构简单,而且技术性能尤其是泛化能力与神经网络相比有明显提高,较全面地反映了粉煤灰的活性,从而为粉煤灰的综合利用及优化配置奠定了基础。     

基于最小二乘支持向量机的天然气负荷预测

对城市天然气负荷预测的研究,对于保证天然气管网用气量、优化管网的调度和设备维修具有极其重要的意义．在国内,对于城市天然气负荷预测的研究才刚刚起步,目前还没有较系统的理论．同技术与理论较为成熟的电力负荷预测研究相比较,两者既有许多相同点,又有不同之处．相同之处在     

采空区塌陷预测的支持向量机方法

针对采空区坍塌预测中诸多因素不确定性问题,应用支持向量机理论并结合工程实际.建立了采空区塌陷预测的支持向量机(SVM)模型.选取覆盖层类型、厚度、矿层倾角、地质构造、采空区距地表的垂直深度、体积率、空间叠置层数等7个影响因子作为采空区塌陷预测的SVM模型的判别因子,利用支持向量机结构风险最小化原则,在某矿区采空区实测数...     

基于支持向量机的精馏塔模糊预测控制算法研究

利用模糊预测控制,依据支持向量机对模糊预测控制方法中的预测模型进行训练,以精馏塔的塔顶回流控制为例,通过仿真研究了支持向量机作为预测模型训练方法在模糊预测控制中的应用,得到了较好的控制效果。利用支持向量机与模糊预测控制结合,进一步发挥了信息处理方法在过程控制中的应用。     

支持向量机在型坯挤出壁厚预测中的应用

将支持向量机应用于挤出吹塑过程的一段型坯壁厚分布的预测,并将预测结果与人工神经网络预测的结果进行比较,验证了支持向量机具有更强的泛化能力。     

Weighted support vector machine for quality estimation in polymerization processes

In this paper, a modified version of the Support Vector Machine (SVM) is proposed as an empirical model for polymerization processes modeling. Usually the exact principle models of polymerization processes are seldom known; therefore, the relations between input and output variables have to be estimated by using an empirical inference model. They can be used in process monitoring, optimization and quality control. The Support Vector Machine is a good tool for modeling polymerization process because it can handle highly nonlinear systems successfully. The proposed method is derived by modifying the risk function of the standard Support Vector Machine by using the concept of Locally Weighted Regression. Based on the smoothness concept, it can handle the correlations among many process variables and nonlinearities more effectively. Case studies show that the proposed method exhibits superior performance as compared with the standard SVR, which is itself superior to the traditional statistical learning machine in the case of high dimensional, sparse and nonlinear data.     

基于向量投影的代谢支持向量机乙烯精馏产品质量软测量建模

针对支持向量机(SVM)增量学习过程中易出现计算速度慢、稳定性差的缺陷,提出了一种基于向量投影的代谢支持向量机建模方法.该方法首先运用向量投影算法对训练样本进行预选取来减少样本数量,提高SVM建模速度.然后将新增样本"代谢"原则引入SVM增量学习过程中,以解决因新增样本不断加入而导致训练样本数量"爆炸"的问题.最后将该方法用于乙烯精馏产品质量软测量建模,实验结果表明,与传统SVM和最小二乘支持向量机(LSSVM)相比,向量投影的代谢SVM具有更好的预测结果.     

支持向量机在丁苯橡胶合成及加工中的应用

采用支持向量机（SVM）与粒子群寻优算法,建立了丁苯橡胶SBR 1712聚合过程和硫化工艺的模拟模型。结果表明,以SBR 1712产品性能胶乳固含量、门尼黏度和分子量分布系数作为输入,乳液间歇聚合所采用的引发剂、活化剂、链转移剂用量分别作为输出的预测模型,训练集及测试集的决定系数均大于0.8,模型预测值与实验值相吻合。以SBR 1712硫化加工中产品的邵尔A硬度、焦烧时间、最小弹性转矩、最小黏性转矩作为输入变量,建立了以填料炭黑用量作为输出的SVM模型,模型拟合及预测性能效果较好。     

Flash point prediction of organic compounds using a group contribution and support vector machine

The flash point is one of the most important properties of flammable liquids. This study proposes a support vector regression (SVR) model to predict the flash points of 792 organic compounds from the DIPPR 801 database. The input variables of the model consist of 65 different functional groups, logarithm of molecular weight and their boiling points in this study. Cross-validation and particle swarm optimization were adopted to find three optimal parameters for the SVR model. Since the prediction largely relies on the selection of training data, 100 training data sets were randomly generated and tested. Moreover, all of the organic compounds used in this model were divided into three major classes, which are non-ring, aliphatic ring, and aromatic ring, and a prediction model was built accordingly for each class. The prediction results from the three-class model were much improved than those obtained from the previous works, with the average absolute error being 5.11–7.15 K for the whole data set. The errors in calculation were comparable with the ones from experimental measurements. Therefore, the proposed model can be implemented to determine the initial flash point for any new organic compounds.     

基于证据框架的最小二乘支持向量机在精对苯二甲酸生产中的应用

支持向量机是一种基于统计学习理论的新型机器学习方法.本文给出一种考虑损失函数的噪声模型参数β的贝叶斯证据框架最小二乘支持向量机回归算法,通过贝叶斯证据框架自动调整正则化参数和核参数,更好地实现了最小化误差和模型复杂性之间的折中.将提出的算法用于精对苯二甲酸（purified terephthalic acid,PTA）生产过程中的关键指标对羧基苯甲醛（4-carboxybenzaldhyde,4-CBA）含量的预测中,能很好地跟踪4-CBA含量的变化趋势,泛化能力较强,为4-CBA含量的实时预测提供了很好的解决方案.     

用于水泥熟料fCaO预测的多核最小二乘支持向量机模型

针对水泥熟料游离氧化钙(fCaO)含量预测模型辨识的问题,考虑到单一核函数无法显著提高模型精度,采用多项式核函数、指数径向基核函数和高斯径向基核函数组合构建等价核的方法,建立了多核最小二乘支持向量机水泥熟料fCaO预测模型。同时,利用改进的粒子群优化算法对多核最小二乘支持向量机模型的6个待确定参数进行迭代寻优,避免了模型参数人工选取的盲目性。最后将基于改进粒子群的多核最小二乘支持向量机模型应用于熟料fCaO含量的实例仿真。结果表明,建立的水泥熟料fCaO含量预测模型精度高、泛化能力强。     

混炼胶质量预测和排胶控制的GMPLS模型研究

针对混炼胶密炼工艺特性，提出广义多路部分最小二乘（GMPLs）算法，建立混炼胶门尼粘度预测和排胶控制模型，克服了普通最小二乘算法在间歇生产质量控制建模中的局限性。试验结果表明，该模型预测混炼胶质量效果良好，并可根据工艺要求在线调整排胶工艺参数，实现智能排胶控制，提高混炼胶质量。     

Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper,we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures.A total number of 279 experimental data points were extracted from open literature to develop the LSSVM.The input parameters were chosen based on the hydrate structure that each gas species form.The modeling resulted in a robust algorithm with the squared correlation coefficients (R2) of 0.9918.Aside from the excellent statistical parameters of the model,comparing proposed LSSVM with some of conventional correlations showed its supremacy,particularly in the case of sour gases with high H2S concentrations,where the model surpasses all correlations and existing thermodynamic models.For detection of the probable doubtful experimental data,and applicability of the model,the Leverage statistical approach was performed on the data sets.This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.