Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

In many fields such as signal processing,machine learning,pattern recognition and data mining,it is common practice to process datasets containing huge numbers of features.In such cases,Feature Selection(FS)is often involved.Meanwhile,owing to their excellent global search ability,evolutionary computation techniques have been widely employed to the FS.So,as a powerful global search method and calculation fast than other EC algorithms,PSO can solve features selection problems well.However,when facing a large number of feature selection,the efficiency of PSO drops significantly.Therefore,plenty of works have been done to improve this situation.Besides,many studies have shown that an appropriate population initialization can effectively help to improve this problem.So,basing on PSO,this paper introduces a new feature selection method with filter-based population.The proposed algorithm uses Principal Component Analysis(PCA)to measure the importance of features first,then based on the sorted feature information,a population initialization method using the threshold selection and the mixed initialization is proposed.The experiments were performed on several datasets and compared to several other related algorithms.Experimental results show that the accuracy of PSO to solve feature selection problems is significantly improved after using proposed method.

基于PCA/PSO的3T1R并联机构性能优化

根据少自由度并联机构应用广泛的优点,提出了一种3T1R并联机构,该机构具有构型简单、结构对称、定位精度高等特点,可应用于小范围的精密操作,或者是大范围的搬运、分拣以及喷涂等领域。基于方位特征方程的拓扑分析理论,对该并联机构完成了自由度种类以及数目的分析与验证;基于闭环矢量法完成了运动学模型建立,并通过位置正逆解算例验证了运动学的合理性。基于位置逆解方程利用极限边界搜索法分析了3T1R并联机构可达工作空间;通过速度分析建立了速度雅可比矩阵,并根据该矩阵分析机构的定位精度与可操作度性能指标。利用主成分分析(PCA)与粒子群算法(PSO)对3个性能指标进行优化设计,并对优化结果进行了分析,最终优化后可达工作空间体积从0.2933m3提高到0.4231m3,定位精度误差放大因子从15.5044减小至4.4308,可操作度指数从9.7027减小至1.3996。

基于粗糙集理论与PCA-APSO-SVM的沥青路面使用性能预测

针对传统沥青路面使用性能预测精度较低的问题,建立了基于粗糙集理论(rough set,RS)与主成分分析法(principal compoent analysis,PCA)-自适应粒子群算法(adaptive particle swarm optimization,APSO)-支持向量机(support vector machine,SVM)的沥青路面使用性能预测模型。基于沥青路面的时序指标与影响因素指标,建立了11个初始预测指标(包括前3年的路面使用性能、当量轴次、路龄、养护性质、坑槽率、修补率、年降水量、平均气温、日照时数);通过RS属性约减筛选出9个核心指标;利用PCA提取4个主成分,得到了基于4个主成分的数据集;将APSO引入到SVM中,对数据集进行训练,并优化了SVM模型参数;建立了路面使用性能的PCA-APSO-SVM预测模型,并以G6京藏高速甘肃境内某段道路为例,对路面使用性能进行预测。研究结果表明:PCA-APSO-SVM模型预测精度较PCA-PSO-SVM、APSO-SVM、PSO-SVM有较大提高,预测结果与实际情况更加符合,能为路面养护决策提供相关参考。

Aerodynamic multi-objective integrated optimization based on principal component analysis

Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

基于PCA-PSO-ELM模型预测地震死亡人数研究

筛选42个历史地震震例,对地震震级、震源深度、震中烈度、抗震设防烈度、震中烈度与抗震设防烈度之差(ΔL)、人口密度以及发震时刻7个影响指标进行主成分分析(principal components analysis,PCA),构建粒子群优化(particle swarm optimization,PSO)极限学习机(extreme learning machine,ELM)地震死亡人数预测模型。将37个震例数据进行预处理和训练,并使用5个震例数据来检验模型的预测精度。实验结果表明,该PCA-PSO-ELM组合模型的平均误差率为10.87%,相比于PCA-ELM模型和ELM模型,其平均误差率分别降低8.70个百分点和18.38个百分点。因此,采用PCA-PSO-ELM组合模型预测地震死亡人数具有一定的可行性。

融合优化可调Q因子小波变换的改进密度峰值聚类算法

为提升时间序列的聚类精度,提出一种融合优化可调Q因子小波变换的改进密度峰值聚类(improved density peaks clustering based on optimal tunable Q-factor wavelet transform,OTQWT-IDPC)算法,该算法利用可调Q因子小波变换的能量优化选择策略及改进粒子群优化算法确定的最佳Q因子分解时序信号,通过最优特征子带的能量、均值、标准差和模糊熵构建特征子空间,并采用主成分分析降低特征维度,以减少特征冗余。同时,考虑到距离较远而周围密集程度较大的K近邻样本对局部密度的贡献率,引入权重系数及K近邻重新定义DPC的局部密度,并利用共享最近邻描述样本间的相似性。在BONN癫痫脑电信号和CWRU滚动轴承数据集上进行对比实验,结果表明,该算法的聚类精度分别为95%、94%,且Jacarrd、FMI和F_(1)值指标均优于其他对比算法,证明了OTQWT-IDPC算法的有效性。

基于支持向量机的网格化电网负荷预测算法设计

针对电网负荷预测算法预测能力较差、效率偏低的问题,文中提出了一种PCA-PSO-SVM算法。其在经典粒子群算法的基础上引入主元分析法,使模型具有降低数据维度及算法冗余度的特性。同时通过改进的PCA-PSO算法对SVM模型的内置参数进行最优选取,从而使改进后的SVM模型具有最佳的分类性能。在实验测试中,采用PCA算法选取了91%贡献度内的6项数据特征进行样本数据训练。结果表明,相较于其他对比算法,该文算法预测结果的RMSE、MAE与MAPE误差值均为最小,证明其可对网格化电网负荷加以预测。此外,该算法还能提升传统算法的准确度,为电力负荷分配提供有力支持。

薄壁小直径柱/板扩散焊界面超声信号特征分析与缺陷智能识别

为解决薄壁小直径柱/板扩散焊质量超声检测时波幅信号中缺陷与界面信号混叠,难以判断焊接接头是否存在微小缺陷的问题,采用基于粒子群优化的支持向量机技术(PSO-SVM),以不同界面类型的多特征参量为输入,对扩散焊界面进行缺陷识别。首先,使用水浸超声检测系统采集试样的C扫描数据,以金相试验得到的焊接截面为参照,运用快速傅里叶变换、经验模态分解等方法提取无缺陷、焊瘤、未焊合3种界面类型的时域、频域特征值;然后使用主成分分析法(PCA)对多特征参量进行融合得到融合特征值;最后输入到PSO-SVM模型中进行缺陷智能识别,并且与未经过多特征融合的预测结果进行对比分析。结果表明:经过PCA处理后,测试结果中3种类型界面的识别准确率为100%,比未经过PCA处理的测试结果准确率提高4.5%。

基于PCA-PSO-ELM的道路结冰预测模型

路面结冰对道路交通安全产生严重影响,有效预测结冰并采取主动的防冰措施成为解决这一问题的最有效且经济的手段。为提高道路行车安全,实现对路面结冰的精准预测,提出了一种融合主成分分析法(PCA)、粒子群优化算法(PSO)和极限学习机(ELM)的道路结冰预测模型。首先,针对相对湿度、大气压强、空气温度、风速、风向、路面温度、水膜厚度、融雪剂浓度8个影响因素进行主成分分析;然后,提取影响因子的主成分,并设定PSO算法参数、种群规模和迭代次数;通过粒子群优化算法搜索极限学习机模型的最优输入权值和隐含层神经元阈值,从而构建了PCA-PSO-ELM道路结冰预测模型;最后,利用3个路段的路面气象数据对模型进行验证。结果表明:PCA-PSO-ELM结冰预测模型的平均预测准确率达到95.85%,显著优于传统ELM、BP神经网络及SVM;此外,该模型在相同时间的不同路段和不同时间的相同路段上均表现出较高的预测准确率、精确率、召回率及F1分数,表明其具备优秀的泛化能力。PCA-PSO-ELM模型在保证准确率的同时,提高了路面结冰预测结果的稳定性,为有效应对路面结冰问题提供了坚实的理论支持。

基于有限元与改进SVM的飞行器结构无损检测模型设计

针对传统飞行器结构无损检测中存在的准确度低且易造成二次破坏等问题,以有限元仿真为数据基础,提出一种基于改进支持向量机的飞行器结构无损检测模型。该模型使用主元分析法对数据主特征进行分析,解决了有限元仿真数据维度高的问题;利用二叉树的思想改进了传统支持向量机,使其具备多特征分类能力,并对多数据特征加以分类,提高了模型的收敛准确度;还通过引入粒子群算法优化多分类向量机的惩罚因子及核函数参数。实验测试结果表明,所提模型可实现分类器参数的性能优化,平均分类准确率较对比算法提升了约1.4%。

基于GA-IPSO-KPCA和变权组合模型的电动汽车充电方法

需求电压和需求电流是充电桩对电动汽车安全充电的重要依据。然而,随着电池的老化,电池管理系统的数据可能出现错误,使得电动汽车在充电时存在安全隐患。针对该问题,建立最小二乘支持向量机和深度置信网络的组合预测模型,提出一种基于变权组合模型的电动汽车充电方法。首先,针对数据掉线缺失问题,使用K均值和反距离加权方法对数据进行插值;然后,使用改进的混合核主成分分析算法对完整数据进行主成分提取,并使用改进粒子群优化算法自动确定混合核函数的权重。基于真实电动汽车数据的实验结果表明,所提方法能够准确地预测需求电压和需求电流,具有实际意义和可行性。

基于PSO-BP神经网络的经济型二手车估价分析

针对BP神经网络预测二手车价格时易陷入局部极小值以及价格影响因素间存在一定相关性的问题,本文提出了一种基于主成分分析(PCA)和粒子群算法(PSO)优化BP神经网络的价格评估模型。本文将PCA降维后的10个主成分作为影响二手车价格的评估参数。基于BP神经网络建立经济型二手车价格评估模型,并使用粒子群算法优化网络的权值和阈值,进一步提高网络的预测精度。该模型一定程度上克服了BP神经网络的不足,为二手车价格评估提供了参考。

基于PSO-GBDT的基桩缺陷智能识别与定位

低应变反射波法是实现基桩缺陷诊断与健康评估的重要手段。然而,目前该方法检测结果的判断仍采用人工方式进行,而人工进行结果的判断又不可避免会因缺陷波形不明显等因素导致误判或判断不准确等问题。为了解决这一问题,利用梯度提升决策树(GBDT)建立低应变反射波法检测结果与桩身缺陷位置的非线性关系,实现桩身缺陷的快速识别与定位,引入粒子群优化算法(PSO)优化模型关键参数,提高模型的精度与泛化能力。此外,利用核主成分分析(KPCA)算法对低应变反射波的多域特征降维,以此降低模型训练难度。最后,通过大量实测数据验证了该模型的可行性与准确性,结果表明,该模型具备基桩缺陷的快速识别与定位的能力。

基于粒子群优化BP神经网络的汽车4S店客户流失预警

客户流失预警作为防止汽车4S店客户流失的重要手段,不仅为当代车企提供了有效的经济效益保证,也为车企对未来决策带来了新的研究依据。为建立汽车4S店客户流失预警分级标准,该文从客户基本信息、车龄、车辆销售价格、贷款金额、维修保养次数、维修保养时间等29个指标着手,基于粒子群优化BP神经网络算法,建立汽车4S店客户流失预警分级标准模型。该模型首先预测出客户流失概率,然后根据值为0-1之间的概率大小分为1-5共5个等级,其中1表流失可能性很小,5表示流失可能性很大。最终得到测试集客户流失预警从1到5等级的比例分别为71.39%、3.75%、3.50%、5.86%和15.50%。同时,通过训练集中有78.65%的客户未流失作为先验概率,判定预测概率小于等于先验概率为客户未流失,大于先验概率为客户流失,得到该模型总体的准确率为91.71%。

Highway Cost Prediction Based on LSSVM Optimized by Intial Parameters

The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task.Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost.This work sorts out and collects 60 sets of measured data of highway engineering;establishes an expressway cost index system based on 10 factors,including main route mileage,roadbed width,roadbed earthwork,and number of bridges;and processes the data through principal component analysis(PCA)and hierarchical cluster analysis.Particle swarm optimization(PSO)is used to obtain the optimal parameter combination of the regularization parameter c and the kernel function width coefficientin least squares support vector machine(LSSVM).Results show that the average relative and mean square errors of the PCA-PSO-LSSVM model are 0.79%and 10.01%,respectively.Compared with BP neural networks and unoptimized LSSVM model,the PCA-PSO-LSSVM model has smaller relative errors,better generalization ability,and higher prediction accuracy,thereby providing a new method for highway cost prediction in complex environments.

基于PSO-ELM的水平井自喷期“多段式”产量预测方法——以玛湖油田百口泉组致密砾岩油藏为例

准确预测油气井动态产量对油田高效开发意义重大,是单井累产油预测以及部署政策优化的关键。玛瑚油田百口泉组致密砾岩油藏水平井自喷期产量呈“多段式”特征,在实际生产过程中,油气井产量受储层物性、压裂工艺参数等多种因素综合影响,传统产量预测方法及数值模拟法考虑影响因素有限,预测方法适用性差。在产量特征认识基础之上,利用主成分分析法(principal component analysis,PCA)优选油层厚度、地层压力、总砂量、渗透率、压裂簇数及含油饱和度6个主控因素,采用粒子群优化-极限学习机(particle swarm optimization-extreme learning machine,PSO-ELM)的输入权值与隐含层偏置,建立了玛湖油田水平井产量预测模型。预测结果表明,PSO-ELM对比传统预测模型具有计算速度快、泛化能力强、预测精度高的优点,利用该方法预测了5口水平井的单井产量,平均误差在2.14%~5.28%,与实际产量吻合良好。

基于PSO-SVR的重型柴油车NO_(x)排放预测

结合重型汽车国Ⅵ污染物排放法规,采用车载便携式排放测试设备(PEMS)进行了某重型柴油车实际道路排放测试.对测试数据进行数据对齐,剔除无效数据后,采用灰色关联分析提取了对NO_(x)排放影响较大的参数,引入主成分分析(PCA)对输入数据进行降维,引入非线性递减惯性权重粒子群算法(PSO)对支持向量回归(SVR)模型进行优化,最终得到重型柴油车实际道路NO_(x)排放预测模型,测试集均方根误差(RMSE)为1.381 6 mg/s,平均绝对百分比误差(MAPE)为19.88%,决定系数R^(2)为0.908 1.该研究为车载NO_(x)传感器故障诊断以及重型车NO_(x)排放在线监管提供一种可能性方法.

基于KPCA-SVM的S700K转辙机故障诊断方法

针对S700K转辙机动作功率曲线非线性特征多样化、复杂化的特点,提出了一种基于核主成分分析(Kernel Principal Component Analysis,KPCA)和支持向量机(Support Vector Machine,SVM)的智能故障诊断方法。首先,对S700K转辙机的功率曲线进行分析,研究正常曲线变化规律,总结常见故障类型功率曲线的变化现象和故障原因。然后,从功率曲线中提取10种时域特征值组成初始特征数据集,用KPCA算法将特征数据映射到高维特征空间中对其进行PCA降维,得到故障样本的非线性主成分。最后,将得到的非线性主成分作为多分类SVM的输入样本进行故障模式识别。采用粒子群优化(Particle Swarm Optimization,PSO)算法分别对核函数参数和SVM惩罚因子进行优化,提高模型的诊断精度。仿真结果表明,该模型能够有效提取转辙机故障信号的非线性特征,故障诊断精度达到97%,诊断时间较短,适用于准确性、实时性要求更高的提速道岔。