基于径向曲线与支持向量回归的颅骨修复方法

颅骨修复技术是对有缺损的颅骨补全对应的缺损部分,进而实现颅骨形状的完整性。针对高维颅骨数据,采用径向曲线来表示颅骨几何特征,结合最小二乘支持向量回归的方法构建颅骨修复模型。提取完整的三维颅骨模型的径向曲线,将其分为已有径向曲线和缺失径向曲线两部分作为训练样本,采用最小二乘支持向量回归统计模型复原出待修复颅骨的缺失径向曲线,进而合并生成待修复颅骨的完整径向曲线,通过迭代最近点算法将合并的颅骨径向曲线与颅骨统计模型进行匹配生成完整的三维颅骨模型。实验结果表明,该方法的平均误差达到6.834×10^-3,比主成分分析方法降低2.90倍,具有更好的修复效果。     

基于改进混沌理论和ACPSO-LSSVR的短期负荷预测

针对现有混沌理论中嵌入维数和延迟时间的选取难以达到最优,局域预测时邻近预测点的选取不够准确,提出了改进的混沌理论:采用改进的C-C法找到嵌入维数和延迟时间;邻近预测点的选取根据参考相点的演化趋势进行判断。针对最小二乘支持向量回归机LSSVR参数难以确定,提出ACPSOLSSVR:自适应混沌粒子群ACPSO一方面能根据群体早熟收敛程度和个体自适应值来调整惯性权重,另一方面能根据混沌变量的随机性和遍历性进行粒子的初始化,加快优化过程,防止局部极小。采用ACPSO来优化LSSVR的待选参数,提高负荷预测的精度。实例分析验证了该方法的可行性和实用性。     

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

Data-driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low-volume and high value-added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data-driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.     

应用于实时控制系统的线程池调度策略设计

在进行多任务实时控制系统设计时,采用线程池技术是一种有效的解决方法,但必须首先避免超时的发生。为了降低线程完成的超时发生率,采用Half-Sync/Half-Async线程池架构建立实时控制系统的线程池,利用最小二乘支持向量回归机（LSSVR）对线程执行时间进行预测估计,再基于估计结果对线程池线程的分配调度优先级别算法进行设计。性能测试以无线图像传感器网络节点为对象对所设计的LSSVR线程池及其他线程池在不同状态下的超时发生率做了比较,结果表明在大多数应用情况下LSSVR线程池在抑制超时方面具有明显的优越性。     

基于GM-LSSVR机场噪声时间序列预测模型

随着我国民航事业的不断发展,机场噪声问题日益严重.针对机场噪声时间序列预测问题,在分析了噪声数据特点的基础上,提出了一种基于GM-LSSVR的噪声序列预测模型,即将机场噪声时间序列进行分解,并分别建立趋势项的GM(1,1)模型和剩余项的LSSVR模型,用于机场噪声预测值的计算.在某机场实测数据中的应用表明,该方法的准确度明显优于其他预测方法.     

LSSVR ensemble learning with uncertain parameters for crude oil price forecasting

Least squares support vector regression (LSSVR) is an effective and competitive approach for crude oil price prediction, but its performance suffers from parameter sensitivity and long tuning time. This paper considers the user-defined parameters as uncertain (or random) factors to construct an LSSVR ensemble learning paradigm, by taking four major steps. First, probability distributions of the user-defined parameters in LSSVR are designed using grid method for low upper bound estimation (LUBE). Second, random sets of parameters are generated according to the designed probability distributions to formulate diverse individual LSSVR members. Third, each individual member is applied to individual prediction. Finally, all individual results are combined to the final output via ensemble weighted averaging, with probabilities measuring the corresponding weights. The computational experiment using the crude oil spot price of West Texas Intermediate (WTI) verifies the effectiveness of the proposed LSSVR ensemble learning paradigm with uncertain parameters compared with some existing LSSVR variants (using other popular parameters selection algorithms), in terms of prediction accuracy and time-saving.     

一种基于局部模型的非线性多工况过程监测方法

针对复杂工业过程中的非线性、非高斯特性以及多工况问题, 提出了一种基于局部模型的在线统计监测新方法. 首先利用局部最小二乘支持向量机回归 (Least square support vector regression, LSSVR) 模型对过程输出进行预测, 与真实的输出相比较构成残差序列. 然后利用 ICA-PCA 两步特征提取策略, 完整地提取残差的高斯和非高斯信息, 最后用三个统计量 (I2、T2 和 SPE) 对过程进行监测, 建立了一种具有非线性、非高斯特性的多工况过程在线监测算法. 通过对 TE (Tennessee Eastman) 过程的仿真研究, 验证提出的方法是可行、有效的, 并显示出了一定的故障检测能力.     

T型管液压成形加载路径自适应多目标优化

为解决静态代理模型非线性结构优化效率、精度低的问题,采用最小二乘支持向量回归机(LSSVR)模型进行T型管液压成形加载路径自适应多目标优化研究.用一个数值算例说明本文方法的有效性,以管与背压冲头的接触面积最大及管的最大减薄率最小为优化目标,以接触面积大于对标仿真值、最大减薄率小于实验值、高度大于实验值为约束条件进行多目标优化设计.采用拉丁超立方体设计构造初始支持向量回归模型,用自适应法将每次迭代中获得的额外取样点添加到重建的支持向量回归机模型,得到帕累托最优解集.用理想点法,选择一个最优妥协解以供工程师选用.在成形高度没有变差的情况下,自适应多目标优化结果的管与被压冲头接触面积比实验值提高了32.42%,最小厚度比实验值增加了14.97%.表明自适应迭代LSSVR模型能够在少量样本下保证优化设计精度和计算效率.     

基于最小二乘支持向量回归机的无线传感器网络目标定位法

针对RSSI测距误差直接影响无线传感器网络(WSN)目标定位准确度的问题,从目标位置与目标到传感器节点测距矢量的双射关系入手,建立最小二乘支持向量回归机(LSSVR)目标定位的数学模型,提出了一种基于LSSVR的WSN目标定位方法TL-LSSVR.根据虚拟目标坐标和虚拟目标到传感器节点距离矢量构造出训练样本,通过确定学习区域及网格化采样获得训练样本集,采用LSSVR训练得到定位模型,将测量得到的距离矢量输入定位模型实现目标定位.对不同传感器节点数量以及不同节点分布情况下的WSN目标进行了定位实验.结果显示,对于节点随机分布的情况,TL-LSSVR方法的定位误差比最小二乘法减小21.0%～43.1%;对于节点均匀分布的情况,TL-LSSVR方法的定位误差则减小26.5%～48.7%,表明TL-LSSVR方法能有效减小测距误差对定位结果的影响,提高目标定位准确度.     

A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China

Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR with the radial basis function (RBF) kernel is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result for the original hydropower consumption series. In terms of error measurements and statistic test on the forecasting performance, the proposed approach outperforms all the other benchmark methods listed in this study in both level accuracy and directional accuracy. Experimental results reveal that the proposed SD-based LSSVR ensemble learning paradigm is a very promising approach for complex time series forecasting with seasonality.