支持向量机综述

支持向量机是基于统计学习理论的一种新的机器学习方法,因其出色的学习性能在国内外学术界引起了日益广泛的重视。本文介绍了支持向量机的基本原理,回顾了SVM近年来的研究与应用,并给出了总结和展望。     

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。     

基于LSSVM的溶剂油分离建模方法研究

针对传统支持向量机和单一模型建模的缺点,利用某炼油厂溶剂油分离过程中二侧线流量作为建模对象,对最小二乘支持向量机集成学习方法进行了研究。首先利用自适应系数加权模糊(AWFCM)聚类算法对训练样本进行聚类;然后对每一类数据使用最小二乘支持向量机建立子模型,并使用PLS合成函数得到最小二乘支持向量机集成模型;最后通过仿真实验来验证最小二乘支持向量机集成模型预测的精确性。结果表明,该算法在预测精度上有了较大的提高,对过程控制系统中分离效果的预测具有重要指导意义。     

基于最小二乘支持向量机的聚氯乙烯汽提过程预测模型

聚氯乙烯(PVC)汽提过程最显著的特点为具有非线性和时变性,属于复杂的非线性工业控制过程,而支持向量机对于非线性系统控制过程表现出了良好的性能。研究基于最小二乘支持向量机建立了(PVC)汽提过程的温度预测模型,将统计学习理论和结构风险最小化理论应用到PVC生产过程中,对汽提塔温度进行建模和仿真实验,仿真结果表明建模方法有效。     

重复压裂选井选层方法研究

重复压裂选井选层一直是水力压裂设计人员关心的首要问题，针对目前的重复压裂选井选层的验方法和数学方法，分别进行了分析比较。重点针对改进的神经网络方法和基于统计学习理论的最小二乘支持向量机算法进行论述，实践证明，两种方法都能够很好地指导重复压裂选井选层，而在样本“数据有限”的情况下，最小二乘支持向量机算法的精度高于神经网络算法。     

基于深度集成支持向量机的工业过程软测量方法

基于支持向量机（SVM）的软测量建模方法已经在工业过程控制领域得到广泛应用,然而传统支持向量机直接针对原始测量变量建立模型,未能充分挖掘数据的内在特征信息以提高预测精度。针对该问题,本文提出一种基于深度集成支持向量机（DESVM）的软测量建模方法。该方法首先利用深度置信网络（DBN）来对数据进行深层次的信息挖掘,提取出数据的内在特征,然后引入基于Bagging算法的集成学习策略,构建基于深度数据特征的集成支持向量机模型,以提升软测量预测模型的泛化能力。最后通过数值系统和真实工业数据对方法进行应用分析,结果表明本文提出的方法能够有效提升支持向量机软测量模型的预测精度,能够更好地预测过程质量指标的变化。     

基于深度集成支持向量机的工业过程软测量方法

基于支持向量机(SVM)的软测量建模方法已经在工业过程控制领域得到广泛应用,然而传统支持向量机直接针对原始测量变量建立模型,未能充分挖掘数据的内在特征信息以提高预测精度。针对该问题,本文提出一种基于深度集成支持向量机(DESVM)的软测量建模方法。该方法首先利用深度置信网络(DBN)来对数据进行深层次的信息挖掘,提取出数据的内在特征,然后引入基于Bagging算法的集成学习策略,构建基于深度数据特征的集成支持向量机模型,以提升软测量预测模型的泛化能力。最后通过数值系统和真实工业数据对方法进行应用分析,结果表明本文提出的方法能够有效提升支持向量机软测量模型的预测精度,能够更好地预测过程质量指标的变化。     

基于提升支持向量机的多组分气体分析

针对大规模高维气体分析样本难以计算的问题,提出一种提升的支持向量机学习方法.该方法将支持向量机等效为一定的KKT条件的同时,能通过检测样本在训练空间的转移始终保持KKT条件成立,起始训练样本的规模最少可以是2个.在对多组分气体分析的实验中,传统的支持向量机学习方法需要时间34 h左右,而提升支持向量机学习的时间为2.7 h,计算速度提高12.6倍.     

第二十七讲 SVR对小样本缺失数据的挖掘处理

正第二十六讲"支持向量机(SVM)简介及DPS应用操作"简要介绍了SVM的概念、原理、模型、算法及支持向量回归(SVR)案例在DPS系统中的操作应用。由于SVM是基于小样本的统计理论,在小样本案例中,计算结果能获得较好的统计效果。一些情况下,难以获得"充分大"的大样本实验数据,例如:难以安排大范围考察的试验设计的场合,如中试以上规模的装置;实验周期较长的响应,如材料老化、蠕     

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.     

基于支持向量基的关联规则挖掘及其在模拟移动床PX吸附分离过程中的应用

In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining （HARM） algorithm based on support vector machines （SVMs） is proposed. Hyperrectangles rules are constructed on the base of prototypes and support vectors （SVs） under some heuristic limitations. The proposed algorithm is applied to a simulated moving bed （SMB） paraxylene （PX） adsorption process. The relationships between the key process variables and some objective variables such as purity, recovery rate of PX are obtained. Using existing domain knowledge about PX adsorption process, most of the obtained association rules can be explained.     

A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities

Support vector machines (SVMs) based optimization framework is presented for the data-driven optimization of numerically infeasible differential algebraic equations (DAEs) without the full discretization of the underlying first-principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data-driven approach is demonstrated on a two-dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multidimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data-driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey-box optimization algorithm, namely the ARGONAUT framework.     

基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断

This study describes a classification methodology based on support vector machines （SVMs）, which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure （based on minimization of radius/margin bound for SVM＇s leave-one-out errors） into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine （FSVM）. The datasets generated from the Tennessee Eastman process （TEP） simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.     

CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily

Cell proliferation, differentiation and death are controlled by a multitude of cell-cell signals and loss of this control has devastating consequences. Prominent among these regulatory signals is the cytokine superfamily, which has crucial functions in the development, differentiation and regulation of immune cells. In this study, a support vector machine (SVM)-based method was developed for predicting families and subfamilies of cytokines using dipeptide composition. The taxonomy of the cytokine superfamily with which our method complies was described in the Cytokine Family cDNA Database (dbCFC) and the dataset used in this study for training and testing was obtained from the dbCFC and Structural Classification of Proteins (SCOP). The method classified cytokines and non-cytokines with an accuracy of 92.5% by 7-fold cross-validation. The method is further able to predict seven major classes of cytokine with an overall accuracy of 94.7%. A server for recognition and classification of cytokines based on multi-class SVMs has been set up at http://bioinfo.tsinghua.edu.cn/~huangni/CTKPred/.     

基于M估计器的支持向量机算法及其应用

训练样本的准确性对回归分析模型有很大的影响,然而训练样本中难免会出现一些造成分析模型失效的奇异点。 为克服奇异点对回归模型的影响,本文提出了一种基于M估计器的支持向量机（M-SVM）。它采用M估计器的目标函数代替最小二乘支持向量机（LS-SVM）目标函数中的残差平方和,同时提出了M-SVM的迭代求解算法,并将该算法应用于含有奇异点的低维仿真数据回归和汽油近红外光谱定量分析中。实验结果证明,相比于其他的支持向量机,M-SVM具有更好的稳健性和分析精度。     

换热网络设计方法的研究进展

回顾了近年来换热网络设计研究的内容和方法。分析了当前求解换热网络最小公用工程设计的3种方法：传统的给定工艺条件的设计法、最近几年得到很大发展的弹性设计法和控制与工艺一体化设计法。阐述了这些方法各自的优势和尚待改进的问题,并指出换热网络的设计同时应考虑先进控制和动态优化,先进控制与工艺一体化换热网络设计是今后的发展方向。     

面向可持续发展的半导体制造的集成产品和过程控制

Semiconductor fabrication is a manufacturing sequence with hundreds of sophisticated unit operations and it is always challenged by strategy development for ensuring the yield of defect-free products. In this paper, an advanced control strategy through integrating product and process control is established. The proposed multiscale scheme contains three layers for coordinated equipment control, process control and product quality control. In the upper layer, online control performance assessment is applied to reduce the quality variation and maximize the overall product performance (OPP). It serves as supervisory control to update the recipe of the process controller in the middle layer. The process controller is designed as an exponentially weighted moving average (EWMA) run-to-run controller to reject disturbances, such as process shift, drift and tool worn out, that are exerted to the op-eration. The equipment in the process is individually controlled to maintain its optimal operational status and maximize the overall equipment effectiveness (OEE), based on the set point given by the process controller. The ef-ficacy of the proposed integrated control scheme is demonstrated through case studies, where both the OPP (for product) and the OEE (for equipment) are enhanced.