基于智能支持向量回归的瓦斯涌出量预测

进行瓦斯涌出量预测是保障安全生产的一个很重要步骤。由于瓦斯涌出量的非线性、不确定性,其预测是很复杂的一个问题。提出一种新的RGASVR智能模型,即基于实值遗传算法参数优选的支持向量回归模型,并且将提出的模型应用于瓦斯涌出量预测。实验结果表明,所提出的模型比BP神经网络、标准支持向量回归有更高的预测精度,具有较强的实用价值。     

基于智能遗传算法与支持向量回归的人口预测

要建立一个有效的支持向量回归(SVR)模型,支持向量回归的3个参数C,!,"必须预先设定。提出一种新型的遗传算法——智能遗传算法(IGA)对支持向量回归进行参数调节,以达到寻找最优参数的目的,然后和支持向量回归结合得到一种新的IGASVR模型,并应用于城市人口预测。最后,将提出的方法与标准SVR模型和BP神经网络模型进行比较,所得结果表明,该模型训练速度快,并且有较高预测精度,是一种有效的人口预测方法。     

Reducing examples to accelerate support vector regression

With increasing of the number of training examples, training time for support vector regression machine augments greatly. In this paper we develop a method to cut the training time by reducing the number of training examples based on the observation that support vector’s target value is usually a local extremum or near extremum. The proposed method first extracts extremal examples from the full training set, and then the extracted examples are used to train a support vector regression machine. Numerical results show that the proposed method can reduce training time of support regression machine considerably and the obtained model has comparable generalization capability with that trained on the full training set.     

An efficient implicit regularized Lagrangian twin support vector regression

Twin support vector regression (TSVR) and Lagrangian TSVR (LTSVR) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose an efficient implicit Lagrangian formulation for the dual regularized twin support vector regression, called IRLTSVR for short. By introducing a regularization term to each objective function, the optimization problems in our IRLTSVR are positive definite and implement the structural risk minimization principle. Moreover, the 1-norm of the vector of slack variable is replaced with 2-norm to make the objective functions strongly convex. Our IRLTSVR solves two systems of linear equations instead of solving two quadratic programming problems (QPPs) in TSVR and one large QPP in SVR, which makes the learning speed of IRLTSVR faster than TSVR and SVR. Particularly, we compare three implementations of IRLTSVR with existing approaches. Computational results on several synthetic and real-world benchmark datasets clearly indicate the effectiveness and applicability of the IRLTSVR in comparison to SVR, TSVR and LTSVR.     

A method to sparsify the solution of support vector regression

Although the solution of support vector machine is relatively sparse, it makes unnecessarily liberal use of basis functions since the number of support vectors required typically grows linearly with the size of the training set. In this paper, we present a simple post-processing method to sparsify the solution of support vector regression (SVR). The main idea is as follows: first, we train a SVR machine on the full training set; then another SVR machine is trained only on a subset of the full training set with modified target values. This process is done several times iteratively. Experiments indicate that the proposed method can reduce the support vectors greatly while maintaining the good generalization capacity of SVR.     

A method to sparsify the solution of support vector regression

Although the solution of support vector machine (SVM) is relatively sparse, it makes unnecessarily liberal use of basis functions since the number of support vectors required typically grows linearly with the size of the training set. In this paper, we present a simple post-processing method to sparsify the solution of support vector regression (SVR). The main idea is as follows: first, we train a SVR machine on the full training set; then another SVR machine is trained only on a subset of the full training set with modified target values. This process is done several times iteratively. Experiments indicate that the proposed method can reduce the support vectors greatly while maintaining the good generalization capacity of SVR.     

一种改进的在线最小二乘支持向量机回归算法

针对一般最小二乘支持向量机处理大规模数据集会出现训练速度幔、计算量大、不易在线训练的缺点,将修正后的遗忘因子矩形窗方法与支持向量机相结合,提出一种基于改进的遗忘因子矩形窗算法的在线最小二乘支持向量机回归算法,既突出了当前窗口数据的作用,又考虑了历史数据的影响.所提出的算法可减少计算量,提高在线辨识精度.仿真算例表明了该方法的有效性.     

Applying support vector regression to water quality modelling by remote sensing data

This article applies a nonlinear machine learning method, support vector regression (SVR), to construct empirical models retrieving water quality variables using remote sensing images. Based on in situ measurements and high-resolution multispectral SPOT-5 (Satellite Pour l'Observation de la Terre) data, a fittest nonlinear function between input and output was obtained from this method, and SVR model parameters were selected automatically using a genetic algorithm (GA). The relationship between water quality variables – permanganate index (COD_Mn), ammonia-nitrogen (NH₃–N) and chemical oxygen demand (COD) – and spectral components of SPOT-5 data for the Weihe River in China was constructed by the proposed method. Spatial distribution maps for the three water quality variables were also developed. The results show that SVR can implement any nonlinear mapping, and produce better predictions than the traditional statistical multiple regression method, especially when samples are limited. With further testing, SVR can also be extended to hyperspectral remote sensing applications in the management of land and water resources.     

支持向量回归估计性能分析

本文对支持向量机用于回归估计进行了详细的性能分析,得出了不敏感系数、惩罚因子和核函数及其参数是影响支持向量机回归估计性能的主要因素。不敏感系数可控制模型的泛化推广能力,其值的确定应考虑样本可能带有的噪声分布状况,惩罚因子可控制拟合曲线复杂性,核函数宽度系数可影响回归曲线光滑程度。因此,在采用支持向量机回归建模时,应根据建模对象选定合适的参数值,以保证回归建模效果。最后通过对原油实沸点蒸馏曲线的拟合问题验证了分析结果,为进一步研究确定SVM参数的优化方法打下了基础。     

Sparse \varepsilon -tube support vector regression by active learning

A method for the sparse solution of $\varepsilon $ -tube support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on its influence on the accuracy of the model using the active learning principle. The training time can be adjusted by the user by selecting how often the hyper-parameters of the algorithm are optimised. The advantages of the proposed method are illustrated on several examples. The algorithm performance is compared with the performance of several state-of-the-art algorithms on the well-known benchmark data sets. The application of the proposed algorithm for the black-box modelling of the electronic circuits is also demonstrated. The experiments clearly show that it is possible to reduce the number of support vectors and significantly improve the accuracy versus complexity ratio of $\varepsilon $ -tube support vector regression.     

Intuitionistic fuzzy C-regression by using least squares support vector regression

This paper proposes a novel intuitionistic fuzzy c-least squares support vector regression (IFC-LSSVR) with a Sammon mapping clustering algorithm. Sammon mapping effectively reduces the complexity of raw data, while intuitionistic fuzzy sets (IFSs) can effectively tune the membership of data points, and LSSVR improves the conventional fuzzy c-regression model. The proposed clustering algorithm combines the advantages of IFSs, LSSVR and Sammon mapping for solving actual clustering problems. Moreover, IFC-LSSVR with Sammon mapping adopts particle swarm optimization to obtain optimal parameters. Experiments conducted on a web-based adaptive learning environment and a dataset of wheat varieties demonstrate that the proposed algorithm is more efficient than conventional algorithms, such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithms, in standard measurement indexes. This study thus demonstrates that the proposed model is a credible fuzzy clustering algorithm. The novel method contributes not only to the theoretical aspects of fuzzy clustering, but is also widely applicable in data mining, image systems, rule-based expert systems and prediction problems.     

Fuzzy cost support vector regression on the fuzzy samples

This paper presents a new version of support vector regression (SVR) named Fuzzy Cost SVR (FCSVR) with a unique property of operating on fuzzy data where fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown to be superior in the experimental results as a property compared with conventional SVR.     

一种支持向量逐步回归机算法研究

支持向量机是解决非线性问题的重要工具,对多元线性回归模型和支持向量机的原始形式进行比较,拟定从样本子集的多元线性回归模型出发,逐步搜索支持向量,提出了一种建立支持向量回归机的快速算法,以降低核矩阵的规模从而降低解凸二次规划的复杂度;最后,分析了该算法的复杂度,并提供了一个算例。     

Using tabu search to configure support vector regression for effort estimation

Recent studies have reported that Support Vector Regression (SVR) has the potential as a technique for software development effort estimation. However, its prediction accuracy is heavily influenced by the setting of parameters that needs to be done when employing it. No general guidelines are available to select these parameters, whose choice also depends on the characteristics of the dataset being used. This motivated the work described in (Corazza et al. 2010), extended herein. In order to automatically select suitable SVR parameters we proposed an approach based on the use of the meta-heuristics Tabu Search (TS). We designed TS to search for the parameters of both the support vector algorithm and of the employed kernel function, namely RBF. We empirically assessed the effectiveness of the approach using different types of datasets (single and cross-company datasets, Web and not Web projects) from the PROMISE repository and from the Tukutuku database. A total of 21 datasets were employed to perform a 10-fold or a leave-one-out cross-validation, depending on the size of the dataset. Several benchmarks were taken into account to assess both the effectiveness of TS to set SVR parameters and the prediction accuracy of the proposed approach with respect to widely used effort estimation techniques. The use of TS allowed us to automatically obtain suitable parameters’ choices required to run SVR. Moreover, the combination of TS and SVR significantly outperformed all the other techniques. The proposed approach represents a suitable technique for software development effort estimation.     

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.     

Recurrent sparse support vector regression machines trained by active learning in the time-domain

A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on the accuracy of the fully recurrent model using the active learning principle applied to the successive time-domain data. The user can adjust the training time by selecting how often the hyper-parameters of the algorithm should be optimised. The advantages of the proposed method are illustrated on several examples, and the experiments clearly show that it is possible to reduce the number of support vectors and to significantly improve the accuracy versus complexity of recurrent support vector regression machines.     

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。     

结合粗糙集与支持向量回归进行油藏物性参数预测

为了更准确的预测油藏物性3个重要参数:孔隙度,渗透率、饱和度,提出了结合粗糙集属性约简和支持向量机回归的方法.首先用粗糙集理论对测井数据样本属性进行约简,从而选出决策属性,构成新的样本数据.然后用支持向量回归理论对数据样本进行训练,建立支持向量回归模型,并且对测试样本进行预测.实验结果表明,该方法获得了较好的拟舍结果,并且减少了支持向量机在训练中的计算复杂度,提高了物性参数预测的准确率.执行该方法可为油藏开发提供决策依据.     

An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers

This study provides evidence supporting the use of the update strategies for the support vector regression (SVR) model. Firstly, the fitting and interpolation method (FIM) is presented to select SVR parameters, and three infill strategies are adopted to search for update points. Secondly, the infill strategy and parameter selection method are illustrated by test functions that illustrate their dependability. The distribution of update points, the sample density and the proportion of update points are discussed. Finally, the adaptive SVR surrogate model is applied to optimize the protective effect of railway wind barriers. The result shows that the parameter selection method has high stability. On the whole, the accuracy of the adaptive SVR model using a suitable infill strategy will be improved with an increasing proportion of update points if the final number of training points is identical. The optimization result shows an optimal porosity of 0.117 when the height of the railway wind barrier is 2.05 m (full scale).