基于支持向量回归的现代气象雷达寿命预测

为了准确地预测气象雷达使用寿命,提高气象雷达运行可靠性、安全性和可维护性,提出了一种新的基于多元回归的气象雷达使用寿命预测框架.在该框架内,采用了支持向量回归(法来求解气象雷达使用寿命的多元回归问题;提出了基于SVR的气象雷达特征参数选择FSSSVR (Feature Subset Selection SVR)算法去掉冗余和无效的特征参数.实验结果表明,基于SVR方法的预测算法能够准确地预测气象雷达的使用寿命,能够为雷达全寿命周期管理提供参考依据SVR)方     

基于残差预测修正的局部在线时间序列预测方法

 对于复杂的非线性和非平稳时间序列预测,基本的支持向量回归(Support Vecotr Regression,SVR)在线算法无法有效兼顾执行效率和预测精度.本文首先采用局部SVR进行时间序列建模预测,同步计算在线更新序列数据预测的残差,并采用Online SVR对残差序列进行混沌时间序列预测,将预测残差值实时补偿到局部SVR模型预测输出.实验结果表明,新方法在执行效率和预测精度方面较单一Online SVR均显著提高.     

基于ICEEMDAN-PE/FE-IGWO-SVR的混合多步分解短期风速预测方法

针对风电场季节性风速波动性和时间尺度差异引起的预测滞后性问题，提出一种基于ICEEMDAN-PE/FEIGWO-SVR的混合多步分解短期风速预测方法。首先，以改进的自适应白噪声完全集合经验模态分解（ICEEMDAN）法对原始时间序列进行一次分解，得到精确的本征模函数（IMF）；再以排列熵（PE）和模糊熵（FE）联合判别方法对其进行二次混合分解，进一步削弱风速波动性；然后将分解后的数据代入支持向量机（SVR）进行预测。此外，为了找到更优的SVR参数，文中引入非线性动态更新因子和萤火虫算法的吸引机制对灰狼算法进行改进，并基于改进的灰狼算法对SVR参数寻优，进而对某风电场进行夏季短期风速预测，实验结果证明，与传统预测方法相比，该方法对短时突变型恶劣风况预测精度更高，对提高风电并网灵活性调度具有一定的应用价值。     

基于机动特征辅助的MFR状态预测方法

针对多功能雷达（Multi-Function Radar,MFR）状态预测方法存在的鲁棒性、预测正确率不佳的问题,提出一种基于机动特征辅助的MFR状态预测方法.该方法将载机机动信息与常规侦收参数共同作为预测特征集,一方面利用支持向量回归（Support Vector Regression,SVR）和侦收信号特征集,得到常规预测模型,另一方面通过SVR和机动特征集,得到MFR各个状态间的转变概率模型;然后利用D-S证据理论得到最终预测状态.实验结果表明,与SVR和LSR方法相比,平均预测精度分别提高了6.97%和7.2%,同时具备更优异的鲁棒性.此外,提出的预测方法通过进一步的拓展,可应用于机械设备、道路交通等领域.     

联合改进核FCM与智能优化SVR的WSNs链路质量预测

为提高无线传感器网络（WSNs）链路质量预测精度和降低噪声影响,提出了一种联合改进核FCM与智能优化SVR （improved kernel furry c-means and intelligent support vector regression,IKFCM-ISVR）的WSNs链路质量预测方案.首先将基于紧致度和离散度的有效性指数引入核FCM方法,实现样本集聚类个数自动划分;然后采用改进核FCM方法对链路质量样本数据进行处理,获得样本聚类隶属度;在此基础上,构建群居蜘蛛优化SVR预测模型,采用基于"动态折射"学习机制的群集蜘蛛对模型参数进行优化,得到不同聚类最佳SVR参数组合;最后采用IKFCM-ISVR算法对不同实验场景下的WSNs链路数据进行预测评估.仿真结果表明,同其它预测算法相比,该算法预测精度提高了36.8~68.4%.     

基于Lasso回归和SVR模型的消费者信心指数的预测

消费者信心指数是反映消费者消费趋向的重要指标,为了掌握消费者信心指数的发展趋势,本文对消费者信心指数进行预测.首先,构建与消费者信心指数相关的关键词作为其影响指标;其次,收集2011～2019年的百度指数数据,并采用Lasso回归等方法对变量进行筛选;最后,建立SVR模型进行预测,并比较在使用不同核函数时,SVR模型的预测效果.结果表明,使用高斯核函数时,SVR模型的预测效果最好,能够较好地预测消费者信心指数,从而为有关部门政策的制定提供参考.     

基于INTLBO-SVR的低压断路器热脱扣时间预测

热脱扣时间是低压断路器的关键指标,利用断路器生产过程中可检测数据可以实现热脱扣时间的预测。针对支持向量回归(SVR)进行热脱扣时间预测,参数的选择对预测的精度和泛化性能影响较大问题,提出一种基于隔离小生境教学算法(Isolated Niche Teaching-Learning-Based Optimization Algorithm,INTLBO)优化支持向量回归的热脱扣时间预测方法。该方法针对教学算法易陷入局部最优的缺点,采用隔离机制的小生境技术对其进行改进,然后利用INTLBO寻优找到最优的SVR参数。根据低压断路器生产历史数据,建立基于INTLBO-SVR的热脱扣时间预测模型。仿真结果表明,与TLBOSVR和常规SVR等方法相比,INTLBO-SVR模型具有较好的预测性能。     

遗传算法优化的支持向量机回归计算老龄化人口方法

老龄化问题对中国经济、就业、医疗等方面的影响越来越大,因此对老龄化人口进行准确预测具有重要的意义.本文利用遗传算法(GA),对支持向量回归模型(SVR)的初始参数进行优化,并利用优化后的SVR模型预测中国老龄化人口.实验证明,使用遗传算法参数寻优后的SVR模型具有良好的预测精度.     

基于EMD-GWO-SVR组合模型的短期风速预测

风速预测对风电场进行调度与控制具有重大意义。针对风速序列的随机性与间歇性，文中提出了EMD-GWO-SVR组合预测模型。先对原始序列进行经验模态分解，并应用GWO算法对支持向量回归模型的参数进行寻优。随后将寻优得到的最佳参数代入支持向量回归模型，并对分解后的本征模函数及残差项分别进行预测，将得到的各预测结果相加从而对风速进行预测。以甘肃省酒泉市的历史气象数据为例，建立BP神经网络、SVR、PSO-SVR、GWO-SVR、EMD-PSO-SVR和EMD-GWO-SVR6种预测模型，对该地的风速进行预测。仿真结果表明，文中提出的EMD-GWO-SVR模型预测精度相比SVR提高了61.759 8%,且其MAE、MAPE和RMSE等误差指标评价值显著低于其它5种模型。     

核偏最小二乘特征提取在混合气体FTIR光谱定量分析中的应用

为进一步提高FTIR光谱法实现特征吸收光谱严重重叠的甲烷、乙烷、丙烷、异丁烷、正丁烷、异戊烷以及正戊烷七组分混合气体定量分析的精度和速度,提出一种核偏最小二乘(Kernel Partial Least square,KPLS)特征提取耦合支持向量回归机(Support Vector Regression Machine,SVR)的红外光谱定量分析新方法.首先采用KPLS方法对上述七组分混合气体的FTIR光谱进行特征提取,然后将特征提取得到的特征组分作为SVR的输入建立混合气体的定量分析模型.对标准混合气体进行定量分析的结果显示:KPLS-SVR模型的预测精度高于未进行特征提取SVR模型预测的精度,同时预测时间也减少了一半.研究表明,KPLS法可以很好地提取隐含在混合气体FTIR光谱数据与其组分浓度之间的非线性特征并有效地消除光谱数据噪声,大幅度降低数据维数,与SVR耦合可以提高红外光谱分析的精度和速度,是一种有效的红外光谱定量分析方法.     

Support Vector Regression Model for Measuring the Permittivity of Asphalt Concrete

The support vector regression (SVR) model for waveguide method of measuring the permittivity of asphalt concrete is presented in this letter. To validate the SVR model, simulated and measured data are employed. The training and testing data for the simulated SVR model are obtained by the reflection coefficient expression. While the testing data for the measured SVR model is obtained by HFSS. Experimental results suggest that the SVR model has a better performance in predicting the permittivity among microwave band. This SVR model could be applied to microwave industry as a kind of permittivity measurement tool.     

A performance comparison of trained multilayer perceptrons andtrained classification trees

The important differences between multilayer perceptrons and classification trees are considered. A number of empirical tests on three real-world problems in power-system load forecasting, power-system security prediction, and speaker-independent vowel recognition are presented. The load-forecasting problem, which is partially a regression problem, uses past trends to predict the critical needs of future power generation. The power-security problem uses the classifier as an interpolator of previously known states of the system. The vowel-recognition problem is representative of the difficulties in automatic speech recognition caused by variability across speakers and phonetic context. In all cases even with various sizes of training sets, the multilayer perceptron performed as well as or better than the trained classification trees. It is therefore concluded that there is not enough theoretical basis to demonstrate clear-cut superiority of one technique over the other     

Support Vector Regression Model for Millimeter Wave Transitions

In this paper, we introduce a new method, support vector regression (SVR) method, to model millimeter wave transitions. SVR is based on the structural risk minimization (SRM) principle, which leads to good generalization ability for regression problem. The SVR model can be electromagnetically developed with a set of training data and testing data which produced by the electromagnetic simulation. Two Ka-band millimeter wave transitions, i.e., waveguide to microstrip transition and coaxial to waveguide adapter, are used as examples to validate the method. Experimental results show that the developed SVR models have a good predictive ability, and they are useful for interactive CAD of millimeter wave transitions.     

Use of Support Vector Regression in Stable Trajectory Generation for Walking Humanoid Robots

This paper concerns the use of support vector regression (SVR), which is based on the kernel method for learning from examples, in identification of walking robots. To handle complex dynamics in humanoid robot and realize stable walking, this paper develops and implements two types of reference natural motions for a humanoid, namely, walking trajectories on a flat floor and on an ascending slope. Next, SVR is applied to model stable walking motions by considering these actual motions. Three kinds of kernels, namely, linear, polynomial, and radial basis function (RBF), are considered, and the results from these kernels are compared and evaluated. The results show that the SVR approach works well, and SVR with the RBF kernel function provides the best performance. Plus, it can be effectively applied to model and control a practical biped walking robot.     

基于支持向量回归的图像超分辨率重建算法

文章提出一种新的基于支持向量回归(SVR)和稀疏表示的图像超分辨重建算法。SVR对输入数据有良好预测输出类别能力。图像统计表明,图像块可以从过完备字典中通过稀疏线性组合很好的表示。对一幅低分辨率输入图像,可以将图像超分辨问题视为在高分辨图像中估计其像素位置。与传统的支持向量回归方法相比,本文采用的特征是不同类型的图像块的稀疏表示。研究表明,稀疏表示作为特征对噪声有一定的鲁棒性。实验结果表明,本文方法与传统支持向量回归方法相比在图像重建质量上有一定的优势。     

An adaptive network traffic prediction approach for LDoS attacks detection

Low‐rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non‐attack situations, the adaptive normal and abnormal ν‐support vector regression (ν‐SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν‐SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within‐class distance and maximizing the between‐class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν‐SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS‐2 environment show that the adaptive ν‐SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν‐SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.     

Support vector regression via MCMC within evidence framework

This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR??s weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.     

Cyclosporine concentration prediction using clustering and supportvector regression methods

A combined strategy of clustering and support vector regression (SVR) methods is proposed to predict Cyclosporine A (CyA) concentration in renal transplant recipients. Clustering combats the high variability and non-stationarity of the time series and reports knowledge gain in the problem. The SVR outperforms other classical neural networks     

基于SVR的语音变换映射规则研究

该文介绍了语音变换与支持向量回归(SVR)的基本理论。提出了基于多输出支持向量回归的语音变换特征参数映射规则,并对该映射规则进行了仿真实验。对变换后语音所进行的主客观测试表明,该映射规则对比码书映射和高斯混合模型,能够在参数映射离散性和平滑性之间有效折中,提高语音可懂度。