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基于支持向量机的软测量建模 总被引:1,自引:0,他引:1
周志成 《自动化技术与应用》2005,24(8):9-11
本文主要讨论支持向量机方法在聚酯工业过程软测量建模中的应用,分析各类支持向量机算法、参数及核函数的选择对建模精度的影响。 相似文献
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基于支持向量机的软测量技术及其应用 总被引:3,自引:0,他引:3
支持向量机(SVM)是一种基于结构风险最小化原理,具有很好推广性能的学习算法。讨论了基于最小二乘支持向量机(LS-SVM)的软测量数据建模原理和方法,并将其应用在汽车排放的氮氧化合物NOX软测量中。通过与基于神经网络的软测量方法进行比较,结果显示出SVM的明显的优势,特别是对小样本、非线性、高维数一类软测量问题的建模,提供了一种有效的途径。 相似文献
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基于支持向量机软测量技术的应用 总被引:1,自引:0,他引:1
软测量技术在工业过程控制中得到广泛的应用。在软测量建模过程中,基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。在简单介绍最小二乘支持向量机算法的基础上,提出了一种新的改进算法——多输入多输出最小二乘支持向量机算法,将其应用到丙烯腈收率的预测模型中,并且与传统的神经网络算法以及多输入单输出最小二乘支持向量机算法进行建模比较。结果表明,这种算法可以在付出轻微代价的基础上,实现多输入多输出模型的软测量,并取得良好的效果。 相似文献
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污水处理系统是一个包含海量信息的非线性复杂系统。针对污水处理出水水质BOD(生物化学需氧量)、COD(化学需氧量)、TN(总含氮量)等难以在线实时检测等问题,建立了基于在线MIMO-LSSVM(多输入多输出最小二乘支持向量机)和PSO(微粒子群算法)的污水处理软测量模型。仿真结果表明,建立的软测量模型精度高、速度快,能很好地实现污水处理出水指标COD、BOD、TN等参数的实时测量和估计,为污水处理的实时在线控制创造必要的前提条件。 相似文献
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支持向量机(SVM)参数的选择是评价SVM性能的一个很重要的因素。SVM在解决小样本、非线性等问题中起到的效果是很好的。但是,该方法的缺点是在解决大样本数据集时消耗时间长,且易陷入局部最优解。为了降低SVM在这方面的不足,本文提出了遗传算法和粒子群算法相结合(PSOGA)对参数进行优化求解,并将该算法建立的模型应用到实验中。仿真结果说明该方法避免了陷入局部解,提高了收敛速度并缩短了优化时间,是一个很有效的方法。 相似文献
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软测量技术是解决现代复杂工业过程中较难甚至无法由硬件在线测量参数的实时估计问题的有效手段。本文介绍了基于回归支持向量机(SVR)算法的基本原理,并以非线性、时变、大滞后的PTA氧化过程为研究对象,使用SVR算法对4-CBA含量进行了预测。结果表明,与传统预测方法相比,采用SVR算法的预测模型,具有精确度高,泛化能力强等优点,是用于PTA氧化过程中4-CBA含量预测的一种有效的方法,具有很好的应用价值。 相似文献
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基于支持向量机的质量控制软测量建模 总被引:1,自引:0,他引:1
在具体研究支持向量机理论的基础上,提出了一种基于支持向量机的软测量控制方法。针对工业过程变量无法在线测量和大滞后的问题,建立了相应的支持向量机回归模型,将此方法用于合成反应器的质量控制中,实现了输出值的在线预估,并分析了参数调整和核函数的选择对建模的影响,得到了泛化良好的模型仿真结果。 相似文献
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This paper presents a hybrid filter-wrapper feature subset selection algorithm based on particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter model is based on the mutual information and is a composite measure of feature relevance and redundancy with respect to the feature subset selected. The wrapper model is a modified discrete PSO algorithm. This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO. Hence, mr2PSO uniquely brings together the efficiency of filters and the greater accuracy of wrappers. The proposed algorithm is tested over several well-known benchmarking datasets. The performance of the proposed algorithm is also compared with a recent hybrid filter-wrapper algorithm based on a genetic algorithm and a wrapper algorithm based on PSO. The results show that the mr2PSO algorithm is competitive in terms of both classification accuracy and computational performance. 相似文献
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The support vector machine (SVM) has a high generalisation ability to solve binary classification problems, but its extension to multi-class problems is still an ongoing research issue. Among the existing multi-class SVM methods, the one-against-one method is one of the most suitable methods for practical use. This paper presents a new multi-class SVM method that can reduce the number of hyperplanes of the one-against-one method and thus it returns fewer support vectors. The proposed algorithm works as follows. While producing the boundary of a class, no more hyperplanes are constructed if the discriminating hyperplanes of neighbouring classes happen to separate the rest of the classes. We present a large number of experiments that show that the training time of the proposed method is the least among the existing multi-class SVM methods. The experimental results also show that the testing time of the proposed method is less than that of the one-against-one method because of the reduction of hyperplanes and support vectors. The proposed method can resolve unclassifiable regions and alleviate the over-fitting problem in a much better way than the one-against-one method by reducing the number of hyperplanes. We also present a direct acyclic graph SVM (DAGSVM) based testing methodology that improves the testing time of the DAGSVM method. 相似文献
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污水处理出水水质软测量建模研究 总被引:1,自引:0,他引:1
污水水质参数监测技术是限制实时在线控制的一个重要因素。本论文进行了基于神经网络软测量技术的污水处理出水水质软测量建模的研究,目标是解决污水处理厂重要出水水质指标因人工化验检测而产生的严重滞后问题,以实现污水处理出水水质的预测及控制。 相似文献
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High-accuracy positioning is not only an essential issue for efficient running of high-speed train (HST), but also an important guarantee for the safe operation of high-speed train. Positioning error is zero when the train is passing through a balise. However, positioning error between adjacent balises is going up as the train is moving away from the previous balise. Although average speed method (ASM) is commonly used to compute the position of train in engineering, its positioning error is somewhat large by analyzing the field data. In this paper, we firstly establish a mathematical model for computing position of HST after analyzing wireless message from the train control system. Then, we propose three position computation models based on least square method (LSM), support vector machine (SVM) and least square support vector machine (LSSVM). Finally, the proposed models are trained and tested by the field data collected in Wuhan-Guangzhou high-speed railway. The results show that: (1) compared with ASM, the three models proposed are capable of reducing positioning error; (2) compared with ASM, the percentage error of LSM model is reduced by 50.2% in training and 53.9% in testing; (3) compared with LSM model, the percentage error of SVM model is further reduced by 38.8% in training and 14.3% in testing; (4) although LSSVM model performs almost the same with SVM model, LSSVM model has advantages over SVM model in terms of running time. We also put forward some online learning methods to update the parameters in the three models and better positioning accuracy is obtained. With the three position computation models we proposed, we can improve the positioning accuracy for HST and potentially reduce the number of balises to achieve the same positioning accuracy. 相似文献
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基于离散粒子群和支持向量机的特征基因选择算法 总被引:1,自引:0,他引:1
基因芯片表达谱信息,为识别疾病相关基因及对癌症等疾病分型、诊断及病理学研究提供一新途径。在基因表达谱数据中选择特征基因可以提高疾病诊断、分类的准确率,并降低分类器的复杂度。本文研究了基于离散粒子群(binary particle swarm optimization,BPSO)和支持向量机(support vector machine,SVM)封装模式的BPSO-SVM特征基因选择方法,首先随机产生若干种群(特征子集),然后用BPSO算法优化随机产生的特征基因,并用SVM分类结果指导搜索,最后选出最佳适应度的特征基因子集以训练SVM。结果表明,基于BPSO-SVM的特征基因选择方法,的确是一种行之有效的特征基因选择方法。 相似文献