基于LIBSVM和时间序列的区域货运量预测研究

针对区域货运量预测中影响因素多、样本数量小的问题,提出了互信息MI与LIBSVM支持向量回归以及状态空间时间序列相结合的预测方法,采用MI进行高维度特征降维后,以新的低维空间作为样本输入,分别建立LIBSVM支持向量回归和状态空间时间序列预测模型。通过重庆市货运量预测实验结果及对比分析表明,该方法在进行有效预测的同时能够改善预测精度,相对误差约为0.06。     

独立成分分析结合近红外光谱分析用于多组分同时测定

建立了1种基于独立成分分析的多元校正方法,结合近红外光谱分析技术进行多组分同时测定.以甲苯、氯苯和正庚烷构成的混合体系为例进行建模和预测,并将预测结果与主成分回归所得的结果进行了比较.结果主成分回归方法对独立测试集32例样本中甲苯、氯苯和正庚烷浓度的预测平均相对误差分别为4.5030、6.0231及9.6042,而独立成分回归方法对三组分浓度的预测平均相对误差分别为4.3871、4.1465及5.8104,说明本方法具有很强的预测能力,可作为1种实用的多元校正方法.     

基于改进粒子群优化算法的灰色神经网络的铁路货运量预测

针对现有铁路货运量预测方法的不足,提出基于改进粒子群优化算法的灰色神经网络（IPSO-GNN）的铁路货运量预测方法,通过IPSO对常规灰色神经网络（GNN）的白化参数进行优化,改善了GNN的不足,保证了预测精度;同时利用灰色关联分析法,计算了铁路货运量和影响因素间的关联度,以最主要的6个关联因素,建立了基于IPSO-GNN的铁路货运量预测模型。仿真实验结果表明,在铁路货运量预测中此模型预测精度优于常规GNN及其他预测方法,说明此预测方法有效可行。     

基于遗传算法-支持向量机的铁路货运量预测

铁路货运量预测是铁路运输部门一项重要工作.针对建立精确预测模型的困难,结合支持向量机与遗传算法(GA-SVM),提出一种铁路货运量预测新方法.利用遗传算法确定支持向量机中的训练参数,以得到优化的支持向量机预测模型,并利用支持向量机在小样本、非线性中优越的预测性能对铁路货运量进行预测.昆明市1991～2005年铁路货运量数据作为实验数据,并采用RBF神经网络与GA-SVM进行对比分析,实验结果表明,GA-SVM预测精确更高,误差更小,可以更有效地对铁路货运量进行预测.     

航空货运量的优化组合预测模型

以1997年～2007年我国航空货运量的统计数据为基础,采用灰色GM（1,1）模型和回归分析模型进行组合优化,建立了基于诱导有序几何加权平均（IOWGA）算子的航空货运量组合预测模型,并对组合预测模型进行检验。检验结果表明,组合预测模型是有效、可靠的,且具有较高的预测精度,可应用于实际预测。最后利用所建立的预测模型预测了2009年～2012年我国航空货运量。     

基于灰色马尔可夫模型对我国货运量的预测

根据"十二五"交通规划,我国将大力推进公路、水路等重点交通基础设施建设,构建内外畅通的交通运输体系。为促进我国经济社会全面协调可持续发展提供了运输保障,货动量的准确预测显得尤为重要。本文通过分析与货运量增长相关因素的关联度,同时采用R软件编写灰色马尔可夫模型对我国"十二期间"货运量进行预测。     

公路旅游客流量预测的支持向量回归模型

介绍了基于统计学习理论的支持向量机回归原理,为解决公路旅游客流量预测建模中的小样本问题,实现对公路旅游客流量的快速准确预测,提出了基于支持向量机回归模型的公路旅游客流量预测方法,给出了参数优化选取算法。仿真实验表明,该方法具有比神经网络等方法更好的预测精度。说明支持向量回归方法用于公路旅游客流量预测是可行有效的。     

一种平稳化短时交通流预测方法

支持向量机回归模型是以预测噪声具有对称性概率分布为假设条件,而实际的短时交通流数据序列具有非平稳特征,这就使得在采用支持向量机回归模型进行短时交通流预测时,难以保证预测噪声的对称性概率分布,从而会影响到预测精度.针对上述问题,在证明支持向量机回归模型对平稳时间序列的预测噪声具有对称性概率分布的基础上,分别针对平稳化和未平稳化的短时交通流观测序列进行了仿真预测,并对预测结果进行了比对分析.分析结果表明,采用平稳化短时交通流预测方法可将预测的均方根误差降低约21.6％,绝对值误差降低约21.3％,相对误差降低约17.3％,仿真结果验证了所提方法的有效性.     

紫外吸收光谱法检测化学需氧量的方法

为提高化学需氧量检测的准确性和时效性,利用多波长紫外吸收光谱法与偏最小二乘回归相结合的算法预测水样中的化学需氧量,同时考虑了浊度对建模所用的吸光度的影响,对浊度的影响进行了补偿.通过实验分析表明:提出的方法对不同类型的污水水质检测均适用,平均相对误差在5%以内,且预测精度明显优于未经浊度补偿的偏最小二乘回归模型.     

改进粒子群算法优化支持向量机的短期负荷预测

针对支持向量回归机在预测建模中的参数选取问题,提出一种基于混沌自适应策略的粒子群优化支持向量回归机参数的方法.采用混沌映射算法和聚合度自适应判断策略,增强种群的全局寻优性能,提升粒子的多样性,从而避免种群过早收敛.充分考虑天气、节假日、居民消费等因素的影响,提出一种改进的支持向量回归机预测模型并与粒子群算法的支持向量回归机模型进行对比分析.分析结果表明,该预测模型可将预测的均方根误差降低约40％,绝对值误差降低约42％,相对误差降低约46％,仿真结果验证了所提方法优化了支持向量回归机参数,改善了预测效果.     

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。     

SA-SVR在移动通信话务量预测中的应用

根据移动通信话务量的时间序列,采用基于模拟退火(SA)算法对超参数选择的支持向量回归机(SVR)进行建模预测。比较ARIMA、人工神经网络和SVR 3种模型的预测效果,并对比研究网格法、遗传算法和SA 3种SVR超参数选择方法对预测效果的影响。实验结果表明,SA-SVR预测精度高、耗时少,是一种预测移动通信话务量的有效方法。     

Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980–2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R ²). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.     

改进的FS算法选取支持向量回归机参数及应用*

针对支持向量回归机SVR的拟合精度和泛化能力取决于相关参数的选取,提出了基于改进FS算法的SVR参数选择方法,并应用于交通流预测的研究。FS(free search)算法是一种新的进化计算方法,提出基于相对密集度的灾变策略改进FS算法的个体初始位置选择机制,以扩大搜索空间,提高全局搜索能力。对实测交通流量进行滚动预测仿真实验,结果表明该方法优化SVR参数是有效、可行的,与经验估计法和遗传算法相比,得到的SVR模型具有更好的泛化性能和预测精度。     

基于自校正支持向量回归的锌产量在线预报模型及应用

提出了基于自校正支持向量回归的密闭鼓风炉锌产量在线预报模型，以便根据预报结果来调整参数，实现锌产量最大．在该模型中，支持向量回归的数学模型被转换成与支持向量分类一样的格式，然后采用简化的SMO方法训练回归系数向量a-a*和阈值b，并在训练过程中动态调整惩罚系数C．最后，给出锌产量的在线预报算法．仿真结果表明，该预报模型在只有较少的样本数的情况下，在有效误差范围内预报精度能达到90％，且具有很好的实时性．     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

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.     

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。