粒子群优化最小二乘支持向量机的短期负荷预测研究

短期负荷预测是电力系统规划和调度前提.电力负荷预测环境复杂多变,为了尽可能提高短期电力负荷预测的精度.本文针对当今人工短期负荷预测方法存在的缺陷,提出了基于粒子群优化算法最小二乘支持向量机(PSO-LSSVM)的短期负荷智能预测方法,该方法采用粒子群(PSO)算法对最小二乘支持向量机(LSSVM)的惩罚系数与核函数进行...     

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.     

基于最小二乘支持向量机的非线性补偿器

针对工业过程中普遍存在的非线性被控对象,通过最小二乘支持向量机对系统的模型偏差建模,并在此基础上构造非线性补偿器.首先,采用具有RBF核函数的LS-SVM离线建立系统偏差模型,并在系统运行时不断对偏差模型进行在线修正;然后基于此模型在DMC预测控制的基础之上构建补偿器;最后成功应用于智能工厂实验室的多变量液位控制实验装置.     

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。     

基于最小二乘支持向量机的蜡沉积速率预测

考虑蜡沉积影响因素的复杂性和最小二乘支持向量机在小样本预测方面的优势,基于最小二乘支持向量机预测的原理,通过优化最小二乘支持向量机的参数,建立了蜡沉积速率的预测模型,并对蜡沉积速率进行了预测。结果表明：该方法在样本数量较小时仍具有较高的精度,蜡沉积速率的预测值和实验值的吻合程度较好;最小二乘支持向量机建模时可以得到直观的函数表达式,而神经网络方法却不能得到模型的显式表达式,因此该方法具有明显的优势;应用径向基核（RBF）作为核函数时,不同初值的正则化参数?和核函数宽度?对预测结果具有较大影响,使用时应合理选择。     

基于偏最小二乘支持向量机的燃煤电站锅炉效率模型

针对电站锅炉燃烧系统非线性强、变量间强耦舍及信号噪声大等特点,提出了基于电站历史运行数据的锅炉效率建模方法。根据锅炉燃烧的机理选取关键输入变量,利用偏最小二乘原理(PLS)对其进行特征提取,建立锅炉效率与所提取特征之间的最小二乘支持向量机(LSSVM)关系模型,组成一个PLS-LSSVM混合模型,并利用电站实际数据对模型的准确性进行验证。结果表明:PLS-LSSVM模型相比于PLS模型具有更强的泛化能力,相比于LSSVM模型有更好的运行效率。     

基于最小二乘支持向量机的复合肥装置养分含量的软测量建模

针对复合肥装置养分含量无法用常规的传感器在线测量的问题,提出了基于最小二乘支持向量机(LS-SVM)的软测量方法来在线估计养分含量.LS-SVM用等式约束代替传统的标准支持向量机中的不等式约束,求解过程从解二次规划问题变成解线性方程组,求解速度相对加快.工业实例表明LS-SVM所建模型的预测精度较高,能满足实际工业应用的需求.     

基于最小二乘支持向量回归机的光管污垢特性预测

搭建了污垢实验系统以测得管壁温度和出、入口温度等参数,并将这些参数作为模型的输入变量,以污垢热阻值作为模型的输出变量,利用最小二乘支持向量回归机搭建了污垢预测模型,对光管的污垢特性进行了预测。一方面,通过与测量结果相比较,验证所搭建的模型是合理可行的;另一方面,通过对多次预测结果分析比较得出,该模型不但适用于流速、水浴温度、材质等参数为定值的情况,而且当这些参数发生改变时,该模型也是适用的。     

基于最小二乘支持向量机回归的单桩竖向极限承载力预测

基于单桩载荷试验数据,采用最小二乘支持向量机(LSSVM)回归的方法,建立了单桩竖向极限承载力的预测模型.利用文献中桩的载荷试验数据来训练LSSVM模型,并确定了模型参数.研究结果表明,同常用的BP网络相比,LSSVM预测模型具有学习速度快、预测性能较好、选择参数少等优点,是一种有效的预测单桩极限承载力的方法.     

最小二乘支持向量机在汽油调合建模中的应用研究

最小二乘支持向量机作为数据挖掘新方法,对学习样本质量和数量要求低,学习的泛化性更好.采用最小二乘支持向量机对小样本数据LS-SVMs建立油品调合数学模型,对模型进行仿真试验,结果表明采用LS-SVMs建立的模型精确,并具有良好的泛化性能.     

藉助自适应支持向量机为延迟焦化反应过程建模

The performance of support vector regression estimation was studied. It is found that the insensitive factor ε, penalty factor, and the kernel function along with its parameter are the main factors affecting the performance of support vector regression estimation. It remains a critical unsolved problem to determine the parmaeters of SVM. Cross-validation methods are commonly used in practice to decide the parameters of SVM, but they are usually expensive in computing time. A novel adaptive support vector machine (A-SVM) was proposed to determine the optimal parameters adaptively. The algorithms for adaptively tuning parameters of SVM were worked out. A-SVM was successfully applied in modeling delayed coking process. Compared with RBFN-PLSR methods, A-SVM was superior in both fitting accuracy and prediction performance. The proposed algorithms in general may be used in modeling complex chemical processes.     

BIAS AND COVARIANCE OF THE RECURSIVE LEAST SQUARES ESTIMATOR WITH EXPONENTIAL FORGETTING IN VECTOR AUTOREGRESSIONS

Abstract. The recursive least squares (RLS) estimation algorithm with exponential forgetting is commonly used to estimate time-varying parameters in stochastic systems. The statistical properties of the RLS estimator are often hard to find, since they depend in a non-linear way on the time-varying characteristics. In this paper the RLS estimator with exponential forgetting factor is applied to stationary Gaussian vector autoregres-sions and the asymptotic bias and covariance function of the parameter estimates are derived.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

Research on Natural Gas Short-Term Load Forecasting Based on Support Vector Regression

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 short-term 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     

ON THE RELATIONSHIP BETWEEN GENERALIZED LEAST SQUARES AND GAUSSIAN ESTIMATION OF VECTOR ARMA MODELS

Abstract. This paper investigates theoretical aspects of the relationship between the generalized least squares and Gaussian estimation schemes for vector autoregressive moving-average models. The asymptotic convergence of the generalized least squares estimator to the Gaussian estimator is established and an alternative numerical method for implementing the generalized least squares scheme is proposed. Finally, some simulation results are presented to illustrate the theory.     

基于最小二乘支持向量机算法的南宋官窑出土瓷片分类

将最小二乘支持向量机(least square support vector machine,LS-SVM)算法用于杭州南宋官窑2窑址出土瓷片的分类研究中,根据瓷片胎和釉的主要、次要和痕量元素组成对它们进行了分类,用留一法检验其分类效果,并与支持向量机( support vector machine,SVM)算法和自组织特征映射(self-organizing map,SOM)算法进行了比较.结果表明:SVM算法和LS-SVM算法比SOM算法更适合于处理"小样本"问题;一般情况下,SVM的分类效果比LS-SVM的分类效果好,但是LS-SVM具有更快的求解速度.     

ON THE CONSISTENCY OF LEAST SQUARES ESTIMATORS FOR A THRESHOLD AR(1) MODEL

Abstract. For the SETAR (2; 1,1) model

where {a_t(i)} are i.i.d. random variables with mean 0 and variance σ²(i), i = 1,2, and {a_t(l)} is independent of {a_t(2)}, we consider estimators of φ₁, φ ₂ and r which minimize weighted sums of the sum of squares functions for σ²(1) and σ²(2). These include as a special case the usual least squares estimators. It is shown that the usual least squares estimators of φ₁, φ₂ and r are consistent. If σ²(1) ≠σ²(2) conditions on the weights are found under which the estimators of r and φ₁ or φ₂ are not consistent.     

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。