Position calculation models by neural computing and online learning methods for high-speed train

For high-speed trains, high precision of train positioning is important to guarantee train safety and operational efficiency. By analyzing the operational data of Beijing–Shanghai high-speed railway, we find that the currently used average speed model (ASM) is not good enough as the relative error is about 2.5 %. To reduce the positioning error, we respectively establish three models for calculating train positions by advanced neural computing methods, including back-propagation (BP), radial basis function (RBF) and adaptive network-based fuzzy inference system (ANFIS). Furthermore, six indices are defined to evaluate the performance of the three established models. Compared with ASM, the positioning error can be reduced by about 50 % by neural computing models. Then, to increase the robustness of neural computing models and real-time response, online learning methods are developed to update the parameters in the last layer of neural computing models by the gradient descent method. With the online learning methods, the positioning error of neural computing models can be further reduced by about 10 %. Among the three models, the ANFIS model is the best in both training and testing. The BP model is better than the RBF model in training, but worse in testing. In a word, the three models can reduce the half number of transponders to save the cost under the same positioning error or reduce the positioning error about 50 % in the case of the same number of transponders.     

Model selection for the LS-SVM. Application to handwriting recognition

The support vector machine (SVM) is a powerful classifier which has been used successfully in many pattern recognition problems. It has also been shown to perform well in the handwriting recognition field. The least squares SVM (LS-SVM), like the SVM, is based on the margin-maximization principle performing structural risk minimization. However, it is easier to train than the SVM, as it requires only the solution to a convex linear problem, and not a quadratic problem as in the SVM. In this paper, we propose to conduct model selection for the LS-SVM using an empirical error criterion. Experiments on handwritten character recognition show the usefulness of this classifier and demonstrate that model selection improves the generalization performance of the LS-SVM.     

Approximate optimization of high-speed train nose shape for reducing micropressure wave

The paper deals with the nose shape design of high-speed railways to minimize the maximum micropressure wave, which is known to be mainly affected by train speed, train-to-tunnel area ratio, slenderness and shape of train nose, etc. It is advantageous to develop a proper approximate metamodel for replacing the real analysis code in the context of approximate design optimization. The study has adopted a newly introduced regression technique; the central of the paper is to develop and examine the support vector machine (SVM) for use in the sequential approximate optimization process. In the sequential approximate optimization process, Owen’s random orthogonal arrays and D-optimal design are used to generate training data for building approximate models. The paper describes how SVM works and how efficiently SVM is compared with an existing Kriging model. As a design result, the present study suggests an optimal nose shape that is an improvement over current design in terms of micropressure wave.     

基于UWB的LSM-Taylor级联车辆定位算法

为了满足智能车在室内的高精度定位要求,针对室内的伪三维定位场景,提出了一种基于超宽带(Ultra Wideband,UWB)的LSM-Taylor级联车辆定位算法.该算法以到达时间差(Time Difference of Ar-rival,TDOA)为定位方式,以多基站最小二乘法(Least Square Method,LSM)定位算法的计算结果为初始值,通过Taylor级数迭代估计车辆的精确位置.该算法主要解决多径效应和非视距产生的测量误差对定位精度的影响,从而提高定位精度.在仿真结果中,相比LSM定位算法,LSM-Taylor级联定位算法的定位结果分布更加紧密,定位精度更高.实际测试结果表明,该定位算法的均方根误差(Root Mean Squared Error,RMSE)在10 cm以下,能满足智能驾驶中的室内定位要求,验证了该方法的有效性.     

Enhancing the Lithium-ion battery life predictability using a hybrid method

This study contributes to proposing the improved bird swarm algorithm optimization least squares support vector machine (IBSA-LSSVM) model to predict the remaining life of lithium-ion batteries. By improving the prediction accuracy of the model, the safety and reliability of the new energy storage system are improved. In order to avoid the bird swarm algorithm (BSA) getting into the local optimal solution, the levy flight strategy is introduced into the improved bird swarm algorithm (IBSA), which improves the convergence performance of the algorithm. Hence, this study is to verify the effectiveness of the proposed hybrid IBSA-LSSVM model. The following work has been done: (1) test functions are used to test particle swarm optimization (PSO), differential evolution algorithm (DE), BSA and IBSA; (2) the back propagation neural network (BP) model, support vector machine (SVM) model, quantum particle swarm optimization support vector machine (QPSO-SVM) model, BSA-LSSVM model and IBSA-LSSVM model are tested with the B5, B6 and B18 batteries. The following findings are obtained: (1) the five test functions are used to test the PSO, DE, BSA and IBSA algorithms in 20 dimensions, 50 dimensions and 80 dimensions. The results show that the convergence accuracy and convergence stability of IBSA algorithm is higher than those of the other three algorithms; (2) the residual life of B5, B6 and B18 batteries are predicted by the BSA-LSSVM, SVM, QPSO-SVM, BP and IBSA-LSSVM models. The test results show that the root mean square error of the IBSA-LSSVM model for B5 battery is 0.01, the root mean square error for B6 battery is 0.06, and the root mean square error for B18 battery is 0.02. The results show that the prediction accuracy of proposed model is higher than that of the other models.     

萤火虫算法优化支持向量机室内定位研究

针对室内定位指纹库匹配冗余信息多造成定位浮动大,且数据库中样本数过多定位时效性差等问题,提出一种基于萤火虫算法FA优化支持向量机SVM的室内定位算法FA-SVM。利用奇异谱分析SSA预处理数据去除噪声,通过萤火虫算法优化支持向量机参数,建立室内定位回归模型。实验结果表明,相对于目前其它室内定位算法,FA-SVM算法收敛速度快,提高了室内定位精度和稳定性。     

基于RDPSO-SVM的粮食产后储藏环节损耗智能评估方法

粮食产后储藏损耗是困扰粮食储藏企业的一大难题,也是影响企业经济效益的重要因素,因此对粮食储藏环节中损耗的评估,对于粮食产后减损具有重要的意义。本文通过调查问卷,对粮食储藏中影响损耗的因素进行调查,将获得的数据通过支持向量机（Support Vector Machine, SVM）模型进行建模,对储藏环节中的粮食损耗进行智能评估。同时,为了提高模型的精度,采用随机漂移粒子群优化（Random Drift Particle Swarm Optimization, RDPSO）算法对SVM的参数进行训练,充分利用RDPSO算法的全局搜索能力找到模型参数的最优解。实验结果表明运用RDPSO算法优化的SVM模型,能够得到比基本的SVM模型和线性回归模型更准确的粮食损耗预测。     

基于最小二乘法的高速列车位置估计

随着高铁列车运营速度的不断提高,列车运行控制系统对列车的定位精度要求也越来越高。每经过一个定位应答器,列车将进行一次位置校核,使定位误差变为0 m。但列车在相邻两应答器间的定位误差会随着列车不断的运行而逐渐增大。针对这一问题,建立了列车位置计算的数学模型,以及高速铁路列车位置估计的速度平均法模型和最小二乘法模型;利用武汉-广州高铁实测数据对模型进行验证。结果表明,与速度平均法模型相比,最小二乘法模型能减少一半的定位误差,能更好地估计列车的位置。     

基于LSSVM的VVP推力系数预报

针对神经网络存在结构较难确定、训练易陷入局部最优以及容易过学习等问题和标准SVM训练速度较慢等问题,提出最小二乘支持向量机算法,最小二乘支持向量机算法(LS-SVM)具有比其他非线性函数逼近方法具有更强的泛化能力;并且LS-SVM采用径向基核函数,得到LSSVM模型的待定参数比标准支持向量机少,仅为2个.将最小二乘支持...     

基于支持向量机识别嗜热和常温蛋白的研究

采用支持向量机对嗜热和常温蛋白进行模式识别并和偏最小二乘回归比较。结果表明,当惩罚因子C为1,核函数选取线性函数,不敏感常数epsilon取0．01时,经320组数据训练,支持向量机预测的平均正确率为84．9％,后者为86．8％。经1720组数据训练,支持向量机对嗜热蛋白预测正确率达97．4％,对常温蛋白预测的正确率为84．2％,平均90．8％。建立了一种基于序列的识别嗜热和常温蛋白的新方法。     

基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法

对于非线性系统预测控制问题, 本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值, 通过输出反馈和偏差校正减少预测误差, 由PSO滚动优化获得非线性系统的控制量. 该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器. 通过对单变量多变量非线性系统进行仿真, 证明了该预测控制方法是有效的, 且具有良好的自适应能力和鲁棒性.     

Multiscale prediction of wind speed and output power for the wind farm

This paper focuses on establishing the multiscale prediction models for wind speed and power in wind farm by the average wind speed collected from the history records. Each type of the models is built with different time scales and by different approaches. There are three types of them that a short-term model for a day ahead is based on the least squares support vector machine (LSSVM), a medium-term model for a month ahead is on the combination of LSSVM and wavelet transform (WT), and a long-term model for a year ahead is on the empirical mode decomposition (EMD) and recursive least square (RLS) approaches. The simulation studies show that the average value of the mean absolute percentage error (MAPE) is 4.91%, 6.57% and 16.25% for the short-term, the medium-term and the long-term prediction, respectively. The predicted data also can be used to calculate the predictive values of output power for the wind farm in different time scales, combined with the generator’s power characteristic, meteorologic factors and unit efficiency under various operating conditions.     

基于联合协同表示与SVM决策融合的高光谱图像分类研究*

针对高光谱图像的分类问题进行了研究,提出一种基于联合协同表示（JCR）与支持向量机（SVM）模型的决策融合分类方法。首先采用联合协同表示模型对样本与字典进行多元素分解并分别进行相应的协同表示,自适应的学习多元素的残差权重并进行线性加权。其次用灰度共生矩阵计算出的统计特征量来训练多类SVM分类器。最后建立一种乘法融合规则将JCR与SVM相结合。在两个标准数据集上的实验结果表明该方法比其他方法具有更好的性能。     

Online option price forecasting by using unscented Kalman filters and support vector machines

This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black–Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can help investors for reducing their risk in online trading.     

基于最小二乘支持向量机误差校正的多方向插值算法

视频在易出错的信道中传输会发生数据丢包和误码等现象,严重影响视频图像质量。为了改善出错图像的质量,首先使用错误隐藏技术中的空域多方向插值方法进行插值,然后利用最小二乘支持向量机学习插值像素点之间的误差相关性来预测插值误差,进而对使用多方向插值方法得到的宏块进行误差校正。实验结果表明,与目前采用的最近像素线性插值法和多方向插值法相比较,基于误差校正的多方向插值算法在错误隐藏视觉效果和峰值信噪比性能指标上都具有一定的优越性。     

基于双重预测模型的非线性时间序列预测

在支持向量机( SVM)预测问题中,为了减小错误参数选取对预测结果的影响,提出了1种基于双重预测模型的非线性时间序列预测算法.该算法在充分考虑支持向量机参数对推广能力影响的基础上,分别利用自回归预测模型(AR)、自回归滑动平均模型( ARMA)、线性回归和决策树模型对SVM参数进行预测,将预测参数运用到SVM预测模型中...     

一种基于WiFi的室内重点区域监测方法

针对无设备的室内重点区域监测问题,本文提出一种Wi-KAM方法,通过获取室内人员的实时位置信息,判断重点区域内部的人员存在情况和区域边界的入侵情况.本方法使用高斯低通滤波算法和主成分分析(PCA)法对提取出的信道状态信息(CSI)进行预处理,并提取位置特征信息.结合最小二乘支持向量机(LSSVM),对样本集进行离线训练和在线分类,获取人员实时位置,实现对重点区域内部及周边人员位置情况的监测.实验表明,本方法可以更精确地进行重点区域内人员入侵检测和位置判别,并提高了室内人员定位的准确性.     

Associated evolution of a support vector machine-based classifier for pedestrian detection

Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work.In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.     

基于改进多输出支持向量机的声发射源定位研究

为了提高根据声发射(AE)现象预报煤与瓦斯突出位置的精度,结合核主成分分析(KPCA),提出了一种改进的多输出最小二乘支持向量机(LSSVM)的目标定位方法.对于采集的声发射参数信号,采用核主成分分析提取重要定位特征;采用多输出最小二乘支持向量机建立定位模型,采用文化基因算法对多输出最小二乘支持向量机参数进行优化.试验测试定位性能,结果表明:算法提高了试验平台声发射定位的精度且定位时间少于其他定位算法,具有很高的实际应用价值.     

改进PSO优化参数的LSSVM燃煤锅炉Nox排放预测

为了提高燃煤锅炉NOX排放浓度预测的准确度,更好地进行氮氧化物的污染监测,提出了一种结合最小二乘支持向量机(Least squares support vector machines,LSSVM)和改进的粒子群优化算法(Particle swarm optimization,PSO)的预测方法。依据LSSVM预测原理及其参数选择的不确定性,采用一种改进的PSO优化算法对模型参数进行寻优,建立锅炉燃烧NOX排放特性模型,并与另两种方法结果进行比较。结果表明:LSSVM是一种有效的建模方法,有较高的拟合度;改进的PSO与LSSVM结合可改善模型的预测精度和泛化能力,在NOX排放浓度预测方面明显优于其他两种参数优化算法,对NOX排放预测有指导意义。