密闭鼓风炉锌产量的支持向量机实时预报模型

为优化密闭鼓风炉的操作参数，建立了锌产量的实时预报模型。该模型采用分类SMO方法训练支持向量机回归模型，并根据若干步的误差来在线校正模型参数，对锌产量进行多步预报，以及时调整操作参数，并能在线学习预报模型。该预报模型的工业仿真表明在只有较少的样本数的情况下，在有效误差范围内能达到90％，且具有很好的实时性。该模型已应用于密闭鼓风炉操作优化与故障诊断系统，能较好地指导生产。     

一种ε可调的在线支持向量回归及其训练算法

标准支持向量回归问题中,噪声较大的时段将包含较多的支持向量。提出一种时间窗内?着可调的支持向量回归方法,根据各时间窗的支持向量的比例动态调整?着,能够处理噪声时变的回归问题。并给出一种?着调整时的在线训练算法,避免重复求解凸规划问题。实例表明该方法的泛化能力和拟合精度较标准支持向量回归为优。     

基于支持向量机的在线建模方法及应用

针对常规v支持向量回归用于在线建模时存在的问题，提出了一种支持向量回归在线建模方法．利用贝叶斯证据框架优化模型参数，通过判断新增观测值是否满足原来的KKT条件，并对历史数据给予不同程度的加权以充分利用最新的数据信息，使模型随着时间的推移在线更新．工业PTA氧化过程中4-CBA含量预测的实例表明，该方法能很好地跟踪4-CBA含量的变化趋势，是一种有效的在线建模方法．     

支持向量回归的一种网络优化策略

提出一种用于支持向量回归的网络优化策略．学习策略分为两个阶段：首先训练支持向量机,得到支持向量回归的初始结构和参数,构造一个无阈值的支持向量回归网络;然后通过带有遗忘因子的递归最小二乘算法,优化计算支持向量回归网络的权值,以达到更好的函数拟合精度．与支持向量回归相比,这种策略可以得到最优的权值和阈值．仿真结果表明,该网络性能优良,具有在线应用的潜力．     

改进的在线支持向量机训练算法

传统支持向量机基于批量训练方法,无法适应环境污染预测中的海量数据与实时性要求。在分析研究一种典型的在线支持向量机回归算法[4]的基础上,指出原算法在训练过程中存在样本重复移动问题,导致模型训练速度下降。提出一种改进算法,消除重复移动问题。实验结果表明,该改进在线支持向量机算法建模精度高,训练速度较原算法有显著提高。     

基于递推最小二乘支持向量回归估计的建模与预报

提出一种新的递推最小二乘支持向量回归估计算法（RLS-SVR），该算法具有实时性高、更新速度快的特点．充分应用RLS-SVR在线学习和训练的实时性，避免辨识模型的维数过高而降低估计精度，本文进一步提出了基于RLS-SVR的混合训练—估计辨识结构．TE过程的组分软测量建模和预报验证了该方法的有效性和优越性．     

基于在线支持向量回归的非线性模型预测控制方法

针对非线性模型预测控制中离线模型难以适应非线性对象实时变化的缺点, 提出一种基于在线支持向量回归的非线性模型预测控制方法. 该方法通过在线支持向量回归离线训练与在线学习相结合的方式, 建立具有在线校正特性的预测模型, 同时采用最速下降原理滚动优化非线性模型预测控制的目标函数, 求得多步控制量. 通过对非线性对象的控制结果表明, 所提出方法有效且具有良好的自适应性.

     

在线稀疏最小二乘支持向量机回归的研究

现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间,不适合在线实时训练.对此,提出一种在线稀疏最小二乘支持向量机回归,其训练算法采用样本字典,减少了训练样本的计算量.训练样本采用序贯加入的方式,适合在线获取,并且该算法在理论上是收敛的.仿真结果表明,该算法具有较好的稀疏性和实时性,可进一步用于建模与实时控制等方面的研究.     

一种基于Cholesky分解的动态无偏LS-SVM学习算法

针对最小二乘支持向量机用于在线建模时存在的计算复杂性问题,提出一种动态无偏最小二乘支持向量回归模型.该模型通过改进标准最小二乘支持向量机结构风险的形式消除了偏置项.得到了无偏的最小二乘支持向量机,简化了回归系数的求解.根据模型动态变化过程中核函数矩阵的特点,设计了基于Cholesky分解的在线学习算法.该算法能充分利用历史训练结果,减少计算复杂性.仿真实验表明了所提出模型的有效性.     

基于SVM的精确数-区间数回归模型建模方法

分析了现有的精确数输入和区间数输出回归算法存在的问题，提出了基于支持向量机的区间数回归建模算法,该算法把支持向量机从精确数回归分析方法推广到区间数回归分析建模方法，在小样本训练集下回归模型具有良好的泛化性能，有效地避免了现有算法中回归模型的下界可能大于上界的问题．以连续退火生产过程中冷却段出口带钢温度预测为例，通过仿真说明了该算法的有效性．     

Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms

Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.     

用支持向量机预测中药水提液膜分离过程

为了找出中药水提液膜过程中影响膜污染的主要原因和预测膜污染的程度以防止膜污染,研究用支持向量机分类、遗传神经网络于中药水提液膜中属性筛选。以筛选出的主要属性用支持向量机回归建模预测,讨论确定模型参数、模型优化等关键问题,并与神经网络运行结果对比分析。分析结果表明支持向量机回归算法对膜污染度的拟合效果和预测能力均好于对该问题分析的其他方法。     

基于支持向量回归机的区域物流需求预测模型及其应用*

为了提高区域物流需求预测的能力,从区域经济等影响因素指标与区域物流需求之间的内在关系的角度,应用基于结构风险最小化准则的支持向量回归机（SVR）方法, 建立“影响因素—区域物流需求” SVR预测模型来研究预测区域物流需求问题。在选择适当的参数和核函数的基础上,对上海市物流需求量进行预测,发现该方法能获得较小的训练相对误差和测试相对误差。     

中国科技园区经济发展智能预测

指出了中国科技园区的经济发展呈现出高度的非线性特征，常规的经济预测手段很难对其作出准确的预测。支持向量回归实现了样本数据从数据空间到特征空间的映射，具备较强的非线性数据处理能力。提出了基于支持向量回归算法的中国科技园区经济发展预测思路，并建立了相应的数学模型。最后，对中国科技园区未来5年的发展状况作出了预测。     

The robust and efficient adaptive normal direction support vector regression

The recently proposed reduced convex hull support vector regression (RH-SVR) treats support vector regression (SVR) as a classification problem in the dual feature space by introducing an epsilon-tube. In this paper, an efficient and robust adaptive normal direction support vector regression (AND-SVR) is developed by combining the geometric algorithm for support vector machine (SVM) classification. AND-SVR finds a better shift direction for training samples based on the normal direction of output function in the feature space compared with RH-SVR. Numerical examples on several artificial and UCI benchmark datasets with comparisons show that the proposed AND-SVR derives good generalization performance     

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series had not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic immune algorithm (SSVRCIA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average, back-propagation neural network, and seasonal Holt–Winters models. Therefore, the SSVRCIA model is a promising alternative for forecasting traffic flow.     

Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. However, the information of inter-urban traffic presents a challenging situation; the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series have not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic simulated annealing algorithm (SSVRCSA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SSVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN) and seasonal Holt-Winters (SHW) models. Therefore, the SSVRCSA model is a promising alternative for forecasting traffic flow.     

基于线性规划支持向量回归的混沌系统预测

支持向量机是一种基于统计学习理论的新颖的机器学习方法,该方法已经广泛用于解决分类与回归问题。标准的支持向量机算法需要解一个二次规划问题,当训练样本较多时,其运算速度一般很慢。为了提高运算速度,介绍了一种基于线性规划的支持向量回归算法,并由此提出几种新的回归模型,同时将它们应用到混沌时间序列预测中,并比较了它们的预测性能。在实际应用中,可以根据具体情况灵活地选择所需模型。     

Implementing support vector regression with differential evolution to forecast motherboard shipments

In this study, we investigate the forecasting accuracy of motherboard shipments from Taiwan manufacturers. A generalized Bass diffusion model with external variables can provide better forecasting performance. We present a hybrid particle swarm optimization (HPSO) algorithm to improve the parameter estimates of the generalized Bass diffusion model. A support vector regression (SVR) model was recently used successfully to solve forecasting problems. We propose an SVR model with a differential evolution (DE) algorithm to improve forecasting accuracy. We compare our proposed model with the Bass diffusion and generalized Bass diffusion models. The SVR model with a DE algorithm outperforms the other models on both model fit and forecasting accuracy.     

基于SVR的瓦斯传感器故障诊断方法

介绍了回归型支持向量机（SVR）的基本原理,建立了基于SVR的传感器时间预测模型,对时间预测器实行离线训练、在线应用的方法,用训练好的SVR模型模拟煤矿井下瓦斯传感器系统的动态特性,阐述了瓦斯传感器故障诊断和信号恢复的实现过程。仿真结果表明：SVR时间预测器能准确预测和跟踪瓦斯传感器的输出,及时诊断出传感器故障信息,并对传感器信号进行恢复,实验验证了该方法的正确性和有效性。