基于粒子群优化算法支持向量回归预测法的大电网电压稳定在线评估方法

提出基于粒子群优化算法支持向量回归预测法(particle swarm optimization support vector regression,PSO-SVR)的大电网电压稳定在线评估方法,将传统基于深度神经网络(deep neural networks,DNN)模型的电压稳定评估方法改进为PSO优化过的SVR模型,对阻抗模裕度指标进行预测。该方法利用了SVR模型具有学习能力强、泛化错误率低的优点,在小样本的情况下也可以很好地学习到样本中的特征。同时克服SVR模型对于参数调节和函数选择非常敏感的问题,利用PSO算法对SVR模型的超参数进行优化选择,可以让SVR模型更好地学习到电网运行数据和阻抗模裕度值之间的非线性关系。最后,该方法在IEEE 118节点系统进行验证,并与基于DNN模型的评估方法进行比较,验证了其精度水平高于基于DNN模型的方法。

基于动态权重优化的风电机组齿轮箱轴承温度预测模型

为准确预测风电机组齿轮箱轴承的温度状态,结合灰色预测GM(1,N)模型、BP神经网络模型和支持向量回归模型,提出了一种动态权重优化的组合预测模型。通过对3种预测模型的理论分析选择了各自合理的模型结构,并用粒子群算法优化模型参数;预处理齿轮箱轴承温度的原始数据后用指数平滑法确定各单一模型的动态权重系数,建立齿轮箱轴承温度的组合模型;通过滑动窗口法统计分析齿轮箱轴承预测温度的残差,判断齿轮箱轴承的运行状态。研究结果表明:组合模型的各项评价指标均优于单一预测模型,决定系数为0.9772,预测效果更加稳定准确,能够及时监测齿轮箱轴承温度的变化情况。

湖南省财政收入影响因素分析及预测——基于Python软件实现

文章以湖南省2010—2019年地方财政收入经济指标数据为样本数据,通过Lasso特征选择影响财政收入的关键因素,再建立单个属性的灰色预测模型,对已被Lasso特征筛选出的2020年各解释变量的值进行预测,最后通过支持向量回归预测模型得出2020年湖南省财政收入。

基于混合算法的通信用户规模预测

考虑到常规SVR预测模型及GA优化和PSO优化的SVR预测模型具有寻优结果稳定性差,容易陷入局部最优解等问题,将具有极强的鲁棒性能和全局搜索能力、能够快速跳出局部最优解等优点的人工鱼群算法与SVR算法进行混合,建立基于混合算法的预测模型。通过混合后的算法能够有效地使算法更快、更准确地得到全局最优解,避免了常规算法在人工鱼更新位置时没有全局信息,只有局部信息引起的收敛速度慢,精度低等问题。使用该混合算法预测模型以及使用传统的三次曲线拟合法和GA-SVR算法建立通信用户规模预测模型,针对2010—2012年通信用户规模进行预测,实验证明基于混合算法的通信用户规模预测模型的预测精度高,稳定性较好,相比另外两种算法,具有较强的优势。

一种基于机器学习的卫星重叠隐蔽通信方法

针对传统卫星重叠通信中单个掩护信号带宽以及功率容限不够的问题,利用卫星转发器频谱环境中多个掩护信号提出了一种频域分割-子谱功率控制联合优化的多掩护信号重叠通信方法,建立了隐蔽通信信号传输性能和隐蔽性能的双目标优化问题,信关站侧采用感知的历史频谱数据训练生成支持向量机回归预测模型,用来预测不同转发器频谱环境下隐蔽信号的通信性能和隐蔽性能,并将训练好的预测模型下载到通信终端;终端侧利用双目标背包算法将支持向量机回归预测模型预测的隐蔽信号的通信性能和隐蔽性能作为价值因素、掩护信号个数作为背包重量来选择转发器频谱环境中的掩护信号,并且求解出隐蔽信号的频域分割和子频谱的功率控制参数,从而实现终端通信信号隐藏在卫星转发器的频谱环境中的目的。

Load prediction of grid computing resources based on ARSVR method

Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregressive prediction model is devised. And an autoregressive support vector regression（ ARSVR） monitoring method is put forward to predict the node load of the data grid. Finally, a model for historical observations sequences is set up using the autoregressive （AR） model and the model order is determined. The support vector regression（SVR） model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.

Efficient fundamental frequency transformation for voice conversion

In order to improve the performance of voice conversion, the fundamental frequency （F0） transformation methods are investigated, and an efficient F0 transformation algorithm is proposed. First, unlike the traditional linear transformation methods, the relationships between F0s and spectral parameters are explored. In each component of the Gaussian mixture model （GMM）, the F0s are predicted from the converted spectral parameters using the support vector regression （SVR） method. Then, in order to reduce the over- smoothing caused by the statistical average of the GMM, a mixed transformation method combining SVR with the traditional mean-variance linear （MVL） conversion is presented. Meanwhile, the adaptive median filter, prevalent in image processing, is adopted to solve the discontinuity problem caused by the frame-wise transformation. Objective and subjective experiments are carried out to evaluate the performance of the proposed method. The results demonstrate that the proposed method outperforms the traditional F0 transformation methods in terms of the similarity and the quality.

Research on Natural Gas Short-Term Load Forecasting Based on Support Vector Regression

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.

Prediction of dust fall concentrations in urban atmospheric environment through support vector regression

Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

Short-Term Wind Power Prediction Using Fuzzy Clustering and Support Vector Regression

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

Parameter selection in time series prediction based on nu-support vector regression

The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.

On-line forecasting model for zinc output based on self-tuning support vector regression and its application

An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.

改进粒子群算法优化SVR的LIBS钢液元素定量分析

通过激光诱导击穿光谱(LIBS)对钢液表面的不同位置进行激发检测,对得到的光谱数据进行归一化预处理。通过主成分分析法筛选出4个代表性因素,将得到的4个因素作为输入信息,针对钢液中Mn、Ni、Cr和Si四种元素,训练并建立定标模型。利用Cat-fish粒子群(PSO)算法选出最优参数值,最后用测试集来验证模型的预测效果。实验结果表明:Cat-fish PSO-支持向量回归(SVR)的决定系数R^2大于0.95,相对标准偏差RSD均值为3.53%,均方根误差RMSE在1.5%以内;所提模型优于普通SVR预测模型,能够快速精确检测出元素含量。该研究为LIBS在线准确定量分析钢液元素提供了借鉴性较高的优化算法。

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.