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.

Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland

Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.

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。

基于VMD样本熵和LS-SVM的滚动轴承故障诊断

滚动轴承在发生故障时其振动信号会出现调幅、调频现象,表现出非线性非平稳特征,通过变分模态分解(VMD)可以反映轴承故障特征。首先应用VMD将轴承振动信号分解为一系列模态分量,计算各模态分量的样本熵并作为特征向量输入到最小二乘支持向量机(LS-SVM)进行训练,得到其模型;然后分别应用线性、多项式和高斯径向基核函数的LS-SVM模型对轴承正常、内圈故障、外圈故障等3种技术状态的轴承样本数据进行故障模式识别。结果表明,在较少样本的情况下,LS-SVM相比于神经网络,有较高的识别精度,且训练时间短,能够有效识别轴承故障类型。

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic(sEMG) signals. In contrast to the existing methods,considering the non-stationary and nonlinear characteristics of EMG signals,to get the more separable feature set,we introduce the empirical mode decomposition(EMD) to decompose the original EMG signals into several intrinsic mode functions(IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines(LS-SVMs) ,the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features,the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

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.

LS-SVM model based nonlinear predictive control for MCFC system

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.

MOBILE GEO-LOCATION ALGORITHM BASED ON LS-SVM

Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.

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.

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.

Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings

TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.

转炉煤气柜位的多输出最小二乘支持向量机预测

针对钢铁企业转炉煤气系统中煤气柜位的预测问题,提出一种一致T型灰色关联分析确定柜位的主要影响用户,避免了冗余输入因素,降低了预测模型的复杂度.根据多个不同煤气柜同时并网运行的情况,提出多输出最小二乘支持向量机回归算法来建立柜位预测模型.该算法采用等式约束,通过最小化所有输出的单一和整体拟合误差,将其转换为求解一系列线性方程组,得到模型回归函数的权系数和偏置公式表示.现场数据仿真实验结果表明所建预测模型的有效性和实用性,为制定煤气调配方案提供了合理指导.

基于最小二乘支持向量机和遗传算法的热式油水两相流含油率建模

Oil holdup of oil-water two-phase flow was measured by using platinum resistance based on the fluid thermal balance equation.In order to improve the measurement accuracy of oil holdup,the effects of the electrical heater fore-and-aft temperature difference of platinum resistance and total oil-water flux on oil holdup were researched.A least squares support vector machine(LSSVM)model with parameters optimized by genetic algorithm(GA)was proposed,the temperature difference and total flux of oil-water two-phase flow were used as inputs,and the oil holdup was used as output of the LSSVM model and the ideal model of oil holdups was obtained.The oil holdup model based on least squares support vector machine and genetic algorithm(LSSVM-GA) was compared with the theory corrected model and good oil holdup measurement results were obtained.The average measurement error was 0.96% in the range of 5% to 60% oil holdup.

鲁棒最小二乘支持向量机及其在软测量中的应用

针对最小二乘支持向量机在利用产生于工业现场的非理想数据集进行建模预测时,稀疏化模型鲁棒性差的问题,提出了一种基于模糊C均值聚类和密度加权的稀疏化方法.首先通过模糊C均值聚类将训练样本划分为若干个子类;然后计算每个子类中各样本的可能贡献度,依次从每个子类中选取具有最大可能贡献度的样本作为支持向量;最后更新每个样本的可能贡献度,继续从各个子集中增选支持向量,直至稀疏化后的模型性能满足要求.仿真结果和磨机负荷实际应用表明,该方法能够兼顾模型在整体样本集和各工况子集上的性能,在实现模型稀疏化的同时,能够显著改善最小二乘支持向量机模型的鲁棒性.

大型地下洞室极限位移预测与稳定性分析

建立大型地下洞室的随机权重粒子群算法–最小二乘支持向量机(RandWPSO-LSSVM)预测模型,通过实例检验预测模型的可靠性,应用于糯扎渡水电站大型调压井洞室围岩的极限位移预测分析,并与数值计算结果对比分析,综合评价工程区围岩的稳定性。研究结果表明,RandWPSO-LSSVM预测模型所预测的极限位移相对真实值的最大误差为6.72%,误差较小,预测效果较好,满足工程要求;糯扎渡水电站大型调压井地下洞室在设计支护参数情况下数值计算的最大位移为19.45 mm,最大拉应力为0.54 MPa,均分布在五洞交叉口边墙位置,远小于微新风化岩体抗拉强度,塑性区也较少,围岩最大位移小于五洞交叉口边墙位置的极限位移预测值,工程区围岩较稳定;研究结果对提前预估大型地下工程的稳定性具有重要意义,可为合理制订施工决策提供参考。

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.