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.     

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     

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...     

支持向量机在混炼胶质量预测中的应用

介绍支持向量机（SVM）的原理，并试验研究密炼过程中应用SVM模型对混炼胶质量进行预测。结果表明，SVM预测模型所得结果与回归分析法模型的预测结果接近，且具有更强的泛化能力；其预测误差控制在门尼粘度均值的3％以内。     

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。     

基于多核支持向量机的非线性模型预测控制

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.     

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。     

岩爆分级预测的支持向量机方法

根据岩爆发生判别准则的4个指标,在利用收集到的岩爆资料数据的基础上,采用SVM方法对岩爆的等级进行了预测分析.通过对冬瓜山矿的应用分析,表明该岩爆分级方法是可行的.     

基于支持向量机的柠檬酸发酵过程统计建模

鉴于生物发酵过程的高度非线性,且样本采集困难,数据总量较少等,采用支持向量机(SVM)方法,为柠檬酸发酵过程建模,得到最终酸度与相关因素间的定量关系。通过优化建模参数,所建SVM模型具有较高的拟合能力,且预测误差小,稳健性好。实例表明,与人工神经元网络等方法相比较,SVM方法更为优越。     

Illumination correction of dyed fabrics method using rotation forest‐based ensemble particle swarm optimization and sparse least squares support vector regression

Different illuminations adversely affect color difference evaluation of textile images in dyed fabrics. To address the problem, we propose a rotation forest (RF)‐based ensemble particle swarm optimization and sparse least squares support vector regression (RF‐PSO‐SLSSVR) for building an accurate illumination correction model. In our algorithm, grey‐edge is first used to extract the statistics characteristics of the textile image. Second, as the standard LSSVR cannot yield a sparse solution, we develop sparse LSSVR (SLSSVR) by calculating the maximal independent subset in the extracted feature space. Then, SLSSVR is embedded into RF by substituting for the regression tree which is the base learner in the original RF, and the PSO technique is employed to obtain the optimal regularization parameter γ and kernel parameter σ. The final model is obtained by fusing the predictions of the different trees through a weighted average method and RF‐PSO‐SLSSVR is constructed to learn the textile illumination estimation model. To verify the effectiveness of our algorithm, we carry out the experiments on the real dyed fabric images by comparison to several related methods and the performance is measured by the different criterions, including the chromaticity error, the angle error, and the Wilcoxon signed‐rank test. Compared with the traditional SVR and ELM algorithm, the results show that the RF‐PSO‐SLSSVR method reduces ~13.6% and 10.6% over the angle RMSE.     

免疫PSO_WLSSVM最优聚丙烯熔融指数预报

熔融指数（MI）是聚丙烯生产的重要指标,建立可靠的熔融指数预报模型非常重要。针对标准粒子群算法(PSO)在迭代过程中易出现粒子过早收敛而陷入局部最优的缺陷,通过引入免疫系统的抗体选择机制,构造了一种基于免疫机制的免疫粒子群优化算法(ICPSO),来保持更新粒子的多样性,从而克服标准粒子群算法过早收敛的缺陷;然后利用ICPSO方法对鲁棒最小二乘支持向量机预报模型(WLSSVM)进行参数寻优,得到最优的ICPSO_WLSSVM预报模型。以实际聚丙烯生产的熔融指数预报作为实例进行研究,结果表明所提出的ICPSO_WLSSVM模型的有效性和良好的预报精度。     

铅对高炉炉底炭砖的侵蚀机制

为了防治铅对炉底衬砖的侵蚀,对被铅侵蚀的高炉炉底炭砖残砖试样进行了性能测试和显微结构分析,并重点分析了含铅量高的炉底炭砖的显微结构,研究了铅在炭砖中的存在形式和分布状态。结果表明:金属铅可以渗入炉底炭砖的气孔中;铅渗入炭砖对炭砖强度、抗氧化性、抗碱性等性能有明显的不利影响;铅对炭砖的侵蚀机制是铅渗透到炭砖的孔隙中氧化膨胀而破坏砖体;防治铅害的措施是尽量少用铅含量高的入炉原料,炉缸炉底采用超微孔炭砖,强化炉缸炉底冷却。     

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。     

用于发酵过程在线建模的自适应局部最小二乘支持向量机回归方法

提出一种基于自适应局部学习的最小二乘支持向量机回归（LSSVR）在线建模方法。考虑样本间的距离和角度信息以获得更全面合理的相似样本集,推导了采用快速留一法在线优化模型参数的准则,并给出了发酵过程在线自适应模型选择的策略。以链激酶流加发酵过程为例,验证了所提出算法能够从过程的第2批次开始,同时对活性菌体浓度和链激酶浓度进行较准确的在线预报,较普通的局部LSSVR等建模方法具有更高的预报精度和自适应性。     

A novel unified correlation model using ensemble support vector regression for prediction of flooding velocity in randomly packed towers

Traditional empirical correlations and models have found insufficient to predict the flooding velocity accurately mainly because there are many kinds of random packings which exhibit different characteristics. In this work, a novel data-driven modeling method, i.e. ensemble least squares support vector regression (ELSSVR), is proposed to construct a unified correlation for prediction of the flooding velocity for packed towers with random packings. The flooding data are first clustered into several classes by the fuzzy c-means clustering algorithm. Then, several single LSSVR models can be trained using each sub-class of samples to capture the special characteristics. Moreover, a weighted least squares approach is adopted to integrate these single LSSVR models. Consequently, the ELSSVR model can extract the feature information of flooding data effectively and improve the prediction performance. The proposed ELSSVR method is applied to construct a unified correlation for prediction of the flooding velocity in randomly packed towers. The obtained results for several kinds of random packings demonstrate that the ELSSVR-based correlation can obtain better prediction performance, compared with the traditional semi-empirical correlations and artificial neural networks-based models. Finally, a database containing the modeling information of flooding velocity in randomly packed towers of China is provided for academic research.     

Application of support vector regression for developing soft sensors for nonlinear processes

The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input–output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high‐dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy‐to‐use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady‐state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR‐based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least‐square‐based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear‐ARX model‐based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input–output data.     

A chaos‐based iterated multistep predictor for blast furnace ironmaking process

The prediction and control of the inner thermal state of a blast furnace, represented as silicon content in blast furnace hot metal, pose a great challenge because of complex chemical reactions and transfer phenomena taking place in blast furnace ironmaking process. In this article, a chaos‐based iterated multistep predictor is designed for predicting the silicon content in blast furnace hot metal collected from a pint‐sized blast furnace. The reasonable agreement between the predicted values and the observed values indicates that the established high dimensional chaotic predictor can predict the evolvement of silicon series well, which conversely render the strong indication of existing deterministic mechanism ruling the dynamics of complex blast furnace ironmaking process, i.e., a high‐dimensional chaotic system is suitable for representing the blast furnace system. The results may serve as guidelines for characterizing blast furnace ironmaking process, an extremely complex but fascinating field, with chaos in the future investigation. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

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.     

Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.