Future changes in rainfall, temperature and reference evapotranspiration in the central India by least square support vector machine

Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotrans- piration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ETo is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ETo have been shown by downscaling the HadCM3 （Hadley Centre Coupled Model version 3） model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ETo and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine （LS-SVM） model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ETo. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081-2091 and 2091-2099 in maximum temperature and 2091-2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ETo in some decades. Two peaks of the increase are observed in ETo in the April-May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model.In this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed model.Experimental results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating datasets.The model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision Trees.The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

基于提升小波和LS-SVM的大坝变形预测

提出了一种基于提升小波和最小二乘支持向量机的大坝变形预测方法,通过提升小波分析提取大坝监测数据效应量,分别对各效应量使用最小二乘支持向量机模型进行训练预测,再将合成各分量的预测结果作为最终的变形预测结果。算例结果表明,该方法较符合实际情况,具有很高的预测精度和良好的泛化能力。

基于EMD近似熵和LS-SVM的机械故障智能诊断

故障特征提取的精确性和分类识别的高效率是提高故障诊断准确率和速度的关键,针对此问题,提出一种基于经验模式分解(empirical mode decomposition,EMD)近似熵和最小二乘支持向量机(least square support vector machine,LS-SVM)的机械故障诊断新方法。利用EMD良好的局域化特性和近似熵表征信号复杂性规律来量化故障特征,再与LS-SVM相结合进行故障类型识别。首先,对故障振动信号进行EMD分解,得到若干个反映故障信息的本征模函数(intrinsic mode function,IMF);其次,选取前4个IMF的近似熵值作为信号的特征向量;最后将构造的特征向量输入到LS-SVM分类器进行故障类型识别。仿真表明,该方法能有效地提取故障特征,与传统的BP(back propagation)网络相比,具有训练样本少、训练时间短、识别率高等优点。

Online LS-SVM for function estimation and classification

An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.

Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.

LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.

基于鲁棒LS-SVM的控制图模式识别

提出一种基于鲁棒最小二乘支持向量机(LS-SVM)的控制图模式识别方法,并研究其应用于过程质量诊断的可行性、有效性.理论研究和仿真试验结果表明,该方法对于标准的6种控制图模式都具有很高的模式识别率,训练模式识别器所需样本少,且训练结果泛化能力强,计算方法简单迅速. Abstract： A technique based on the robust least squares support vector machines(LS-SVM) used for control charts pattern recognition is proposed, the applied feasibility and validity of this technique in process quality diagnosis is also investigated. Theoretical research and experimental results show that this approach performs well upon the six typical control charts pattern recognition with high recognition accuracy, simple computation and fast training process, and the preeminent generalization ability on the condition of small sample size.

Combined forecast method of HMM and LS-SVM about electronic equipment state based on MAGA

For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.

Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.

基于遗传算法和最小二乘支持向量机的织物剪切性能预测

提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力. Abstract： A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.

基于可见近红外光谱技术的车蜡品牌无损鉴别方法研究

探讨了可见-近红外光谱技术快速无损识别不同品牌车蜡的可行性。实验一共获得104样本,其中40个样本(建模集)用于建立模型,剩余64个样本(预测集)被用于独立验证建立好的模型。基于五种不同品牌车蜡的可见-近红外光谱分别建立了线性判别分析(linear Discriminant Analysis,LDA)和最小二乘支持向量机(least square-support vector machine,LS-SVM)模型。基于两个算法的全波段光谱模型的预测集正确率分别达到了84%和97%。进一步采用连续投影算法(successive projections algorithm,SPA)算法从751波段中选取了7个特征波段(351,365,401,441,605,926和980nm)。基于SPA选择的变量建立LS-SVM模型,准确率依然保持在97%。说明SPA选择的特征波段包含了对于车蜡品牌鉴别最重要的光谱信息,而大多数无用信息则被有效剔除。将SPA与LS-SVM算法的车蜡识别模型在保证正确率的基础上,还可以大大降低模型计算复杂程度,说明该模型能快速准确的从车蜡可见-近红外光谱中提取有效信息,并实现车蜡品牌的无损鉴别。

基于机器视觉的西瓜子外观品质检测与分类

采用机器视觉获取了西瓜子的面积、周长、最小外接矩和圆形度等外形特征,而后使用遗传算法优化的最小二乘支持向量机算法对西瓜子外观品质进行分类识别,最终实现了破损瓜子、普通瓜子和优质瓜子的区分。实验结果表明,基于最小二乘支持向量机分类的西瓜子外形检测方法能够很好地实现西瓜子外观品质的识别检测。

Applying ANN,ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO_(2)

In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.

基于民航团队旅客销售的组合预测方法分析

利用Matlab分别用回归分析算法、BP神经网络算法、最小二乘支持向量机算法和组合预测算法对民航团队销售数据进行预测和比较分析,期望为民航销售人员提供更加精准的预测信息,以获得更高的航线收益。结果显示神经网络、支持向量机和组合预测3种算法比航空公司常用的回归分析预测精准度有了明显的提高。支持向量机预测精度相对神经网络稍低,却拥有更强的泛化能力。组合预测能避免单一预测方法的误差,更加适合航线销售人员的实际操作。

Hybrid connectionist model determines CO_2–oil swelling factor

In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.

Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques

Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump rubber.The DRC analysis requires prolonged laboratory validation.To develop fast and effective DRC determination methods,this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches.This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging.A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples.To obtain an average spectrum to represent a cup lump rubber sample,the spectral data were collected by locating and scanning for point-based and area-based measurement,respectively.The spectral data were calibrated using partial least squares regression(PLSR)and the least-squares support vector machine(LS-SVM)methods against the reference values.The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach.The best predictions of PLSR represented by the coefficient of determination(R2),the root mean square error of prediction(RMSEP)and the residual predictive deviation(RPD)were 0.99,0.72%and 15.17,while the best prediction of LS-SVM were 0.99,0.64%and 16.83,respectively.In summary,the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.

优化的近红外光谱LS-SVM模型测定小麦蛋白质

小麦蛋白质测定的常规方法为湿化学分析法,具有操作复杂、污染环境和耗时较长等缺点,为此建立了一个近红外光谱LS-SVM模型,以实现小麦蛋白质含量的简便、快速测定。首先对样品光谱进行"均值中心化+去趋势"预处理,并用SPXY法划分校正集和测试集样本;然后采用改进的二进制蝙蝠算法(IBBA)进行建模参数和特征波长的联合优化,根据优化结果对校正集数据建立LS-SVM模型,并用测试集数据验证其性能;最后通过与常用的PLS、CARS-PLS及未优化的SVM、LS-SVM建模结果进行比较,确认该模型的有效性。结果表明,该模型的各项性能指标优异,能够满足实际检测工作的要求。

DSC-backstepping based robust adaptive LS-SVM control for near space vehicle’s reentry attitude

Purpose–The purpose of this paper is to propose a robust control scheme for near space vehicle’s(NSV’s)reentry attitude tracking problem under aerodynamic parameter variations and external disturbances.Design/methodology/approach-The robust control scheme is composed of dynamic surface control(DSC)and least squares support vector machines(LS-SVM).DSC is used to design a nonlinear controller for HSV;then,to increase the robustness and improve the control performance of the controller.LS-SVM is presented to estimate the lumped uncertainties,including aerodynamic parameter variations and external disturbances.The stability analysis shows that all closed-loop signals are bounded,with output tracking error and estimate error of LS-SVM weights exponentially converging to small compacts.Findings-Simulation results demonstrate that the proposed method is effective,leading to promising performance.Originality/value-First,a robust control scheme composed of DSC and adaptive LS-SVM is proposed for NSV’s reentry attitude tracking problem under aerodynamic parameter variations and external disturbances;second,the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS-SVM to estimate aerodynamic parameter variations and external disturbances.

基于最小二乘支持向量机的电力系统混沌振荡控制

电力系统在周期性负荷扰动的作用下会发生混沌振荡,甚至由此而失去稳定.为抑制这种情况下的混沌振荡对电力系统的影响,利用支持向量机良好的非线性函数逼近和泛化能力,提出了最小二乘支持向量机(LS-SVM)的电力系统混沌振荡控制方法.运用最小二乘支持向量机对电力系统的动力学特性进行学习,得到训练好的电力系统LS-SVM模型,进而实现对电力系统混沌振荡的控制.该方法不需要被控混沌系统的解析模型,数值仿真结果表明该方法的可行性.