Time series online prediction algorithm based on least squares support vector machine

Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.

Estimating coal reserves using a support vector machine

The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.

Support vector machine method for fore-casting future strong earthquakes in Chinese mainland

Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization （forecasting） ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world, however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.

Support Vector Machine-Based Nonlinear System Modeling and Control

This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.

Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.

A Support Vector Machine Based on Bayesian Criterion

SVM handles classification problem only considering samples themselves and the classification effect depends on the characteristics of the training samples but not the current information of classified problem.From the phenomena of data crossing in systems,this paper improves the classification effect of SVM by adding the prior probability item reflecting the classified problem information into the decision function,which fuses the Bayesian criterion into SVM.The detailed deducing process and realizing steps of the algorithm are put forward.It is verified through two instances.The results showthat the new algorithm has better effect than the traditional SVM algorithm,and its robustness and sensitivity are all improved.

Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

支持向量机在纸浆蒸煮过程Kappa值软测量中的应用研究

针对纸浆蒸煮过程机理复杂、影响因素众多和数据不完备条件下纸浆Kappa值预报问题,在介绍支持向量机基本原理和实现算法的基础上,探讨了支持向量机方法在纸浆Kappa值预报中的应用,经过与线性回归方法和人工神经网络方法预报结果比较,表明该方法具有精度高、速度快、泛化能力强的特点,取得了较传统建模方法更好的预报效果。

AUDIO STEGANALYSIS OF DSSS BASED ON STATISTICAL MOMENTS OF HISTOGRAM

Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.

基于多元统计分析的小样本数据预测模型设计

若小样本数据预测误差较大,会直接影响数据应用效果,为提升小样本数据预测精度,提出基于多元统计分析的小样本数据预测模型设计方法。将小样本数据放入SPSS软件中,结合自助法完成小样本数据的经验分布分析。基于样本数据经验分布特征,结合具备学习能力的Fisherface算法对小样本上数据实施预分类,建立测试样本类别标签,实现小样本数据的特征提取。通过多元统计分析数据特征的主元成分,确定模型回归函数,结合支持向量机构建数据预测模型,通过上述模型完成小样本数据的精准预测。实验结果表明,使用上述方法开展小样本数据预测时,预测误差较低,效率较高,说明其预测效果较好。

基于先验统计模型的非侵入负荷辨识算法

针对传统非侵入负荷辨识技术中电热细分能力不足的问题,文中提出了一种基于先验知识与统计学习模型的居民非侵入式负荷辨识算法。文中对洗衣机辅热、电水壶、电饭锅、电热水器等设备进行了电热细分研究,通过设备运行关联算法实现了辅热设备的细分,并在用户有限反馈信息和专家标注的基础上,实现了非辅热设备分类的模型训练。实验结果表明,文中所提技术框架在事件检测负荷辨识算法的基础上实现了电热设备的细分,且在运行状态分解的F1分数指标中取得了0.9以上的优异效果。

基于统计学内容与特征分析的通信信息自动化检测系统研究

信息技术的快速发展在为人们的日常生活带来便利的同时给网络安全带来较多隐患。为提高现有通信信息安全保护技术的水平,从统计学角度出发,根据多元统计分析、相关性分析以及主成分分析的方法统计和降维网络流量数据,并引入支持向量机(Support Vector Machine,SVM)进行改进,提出一种新型通信信息自动化检测模型。实验结果表明,多元统计相关性降维分析法下的数据统计和降维效果最明显,同时检测模型的准确率最高为92.4%,召回率最高为85.7%,F1值最高为89.1%,误报率最低为8.4%。由此可知,研究所提方法具有一定的可行性和优越性,能够为通信信息安全保护技术提供一种新的方法。

支持向量机理论与算法研究综述

统计学习理论(statistical learning theory,SLT)是一种小样本统计理论,着重研究在小样本情况下的统计规律及学习方法性质。支持向量机(support vector machinse,SVM)是一种基于SLT的新型的机器学习方法,由于其出色的学习性能,已经成为当前机器学习界的研究热点。该文系统介绍了支持向量机的理论基础,综述了传统支持向量机的主流训练算法以及一些新型的学习模型和算法,最后指出了支持向量机的研究方向与发展前景。

支持向量机理论及算法研究综述

介绍了SVM的理论基础和它的多种主要算法及这些算法的利弊与发展现状,并介绍了SVM在现实生活中的应用原理及应用现状。最后分析了SVM在发展中的不足之处,指出了其研究方向及前景,并提出在分布式支持向量机这个方向上可以进行更深层次的研究。

支持向量机在小样本识别中的应用

针对癌症细胞诊断过程中样本采集困难 ,数目偏少的实际情况 ,在癌症的早期诊断中引入了一种新的模式识别方法———支持向量机 该方法基于统计学习理论的原理 ,较好地解决了小样本的学习分类问题 ,通过对具有不同性状的癌前增生细胞进行分类识别验证 ,支持向量机取得了较传统分类方法更好的识别效果 .

基于支持向量机的渐进直推式分类学习算法

支持向量机(support vector machine)是近年来在统计学习理论的基础上发展起来的一种新的模式识别方法,在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势.直推式学习(transductive inference)试图根据已知样本对特定的未知样本建立一套进行识别的方法和准则.较之传统的归纳式学习方法而言,直推式学习往往更具普遍性和实际意义.提出了一种基于支持向量机的渐进直推式分类学习算法,在少量有标签样本和大量无标签样本所构成的混合样本训练集上取得了良好的学习效果.

支持向量机的新发展

Vapnik等学者首先提出了实现统计学习理论中结构风险最小化原则的实用算法—支持向量机 ,比较成功地解决了模式分类问题 .其后 ,机器学习界兴起了研究统计学习理论和支持向量机的热潮 ,引人瞩目的研究分支有从最优化技术出发改进或改造支持向量机 ,依据统计学习理论和支持向量机的优点设计新的非线性机器学习算法等 .对此 ,较为系统地回顾了近

支持向量机训练算法综述

本文介绍统计学习理论中最年轻的分支——支持向量机的训练算法 ,主要有三大类 :以 SVM-light为代表的分解算法、序贯分类方法和在线训练法 ,比较了各自的优缺点 ,并介绍了其它几种算法及多类分类算法 .最后指出了支持向量机具体实现的方向及其在模式识别、数据挖掘。

基于支持向量机的入侵检测系统

目前的入侵检测系统存在着在先验知识较少的情况下推广能力差的问题.在入侵检测系统中应用支持向量机算法,使得入侵检测系统在小样本(先验知识少)的条件下仍然具有良好的推广能力.首先介绍入侵检测研究的发展概况和支持向量机的分类算法,接着提出了基于支持向量机的入侵检测模型,然后以系统调用执行迹(system call trace)这类常用的入侵检测数据为例,详细讨论了该模型的工作过程,最后将计算机仿真结果与其他检测方法进行了比较.通过实验和比较发现,基于支持向量机的入侵检测系统不但所需要的先验知识远远小于其他方法,而且当检测性能相同时,该系统的训练时间将会缩短.