基于遗传优化最小二乘支持向量机的导引头故障诊断

为提高导引头故障诊断准确率,提出了一种采用改进遗传算法优化的最小二乘支持向量机（LSSVM）构造导引头多故障分类模型的方法。该方法基于一对一策略及改进的投票法建立两层LSSVM多故障分类器,并利用一种自适应变步长搜索策略改进的遗传算法对LSSVM的核参数和正则化参数进行自动优选。通过对某型导引头实测数据的仿真并和标准SVM及BP神经网络诊断方法相比较,结果表明该方法具有更高诊断准确率和计算效率。     

基于支持向量机和遗传算法的人脸识别研究

采用自适应遗传算法(AGA)优化筛选改进高斯核函数支持向量机(SVM)参数模型进行人脸特征分类。支持向量机的泛化性能主要取决于核函数类型和核函数参数及惩罚系数C,本文在传统高斯核函数基础上提出改进高斯核函数作为支持向量机的非线性映射函数,并使用自适应遗传算法优化筛选核函数参数和支持向量机惩罚系数,将优化后的SVM模型用于人脸库进行实验仿真。实验结果表明,本文方法比传统高斯核函数支持向量机分类器模型有更高识别率。     

Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy wavelet support vector classifier machine to diagnosing the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory, wavelet analysis theory and v-support vector classifier machine, fuzzy wavelet v-support vector classifier machine (FWv-SVCM) is proposed. To seek the optimal parameters of FWv-SVCM, genetic algorithm (GA) is also applied to optimize unknown parameters of FWv-SVCM. A diagnosing method based on FWv-SVCM and GA is put forward. The results of the application in car assembly line diagnosis confirm the feasibility and the validity of the diagnosing method. Compared with the traditional model and other SVCM methods, FWv-SVCM method requires fewer samples and has better diagnosing precision.     

Housing price forecasting based on genetic algorithm and support vector machine

Accurate forecasting for future housing price is very significant for socioeconomic development and national lives. In this study, a hybrid of genetic algorithm and support vector machines (G-SVM) approach is presented in housing price forecasting. Support vector machine (SVM) has been proven to be a robust and competent algorithm for both classification and regression in many applications. However, how to select the most appropriate the training parameter value is the important problem in the using of SVM. Compared to Grid algorithm, genetic algorithm (GA) method consumes less time and performs well. Thus, GA is applied to optimize the parameters of SVM simultaneously. The cases in China are applied to testify the housing price forecasting ability of G-SVM method. The experimental results indicate that forecasting accuracy of this G-SVM approach is more superior than GM.     

基于捕食搜索策略遗传算法的SVM参数优化方法

基于支持向量机(SVM)模型的泛化能力和拟合精度与其相关参数的选取有关,提出将捕食搜索策略的遗传算法(PSGA)运用到SVM的参数选取中。该算法以最小化输出量的拟合误差为目标,以SVM的3个参数作为决策变量。通过对谷氨酸发酵过程建模的实验表明,该方法可以提高谷氨酸浓度的训练精度及预测精度,是一种优化SVM参数的有效方法。     

基于EMD多模态特征融合支持向量机的故障诊断

针对非平稳时间序列信号,提出一种基于经验模态分解(EMD)的特征提取和多模态特征融合支持向量机的故障诊断方法,首先对原始信号进行EMD分解,选择能量最大的几个基本模式分量(IMF)并提取其小波包特征,获得对每个IMF独立的特征子集;然后在每个IMF特征子集中训练SVM弱分类器,并根据各特征子集对应的IMF能量权重进行加权融合,获得故障状态的强分类器,将该方法应用于6135型柴油机振动信号故障诊断中,实验结果表明了其可行性和有效性.     

基于遗传算法的支持向量机预测含能材料密度的研究

基于遗传算法(genetic algorithm,GA)的变量筛选和支持向量机(support vector machine,SVM),提出了一种改进的定量结构-性质相关(quantitative structure detonation relationship,QSPR)建模方法——遗传-支持向量机(GA-SVM),并用其建立含能材料的定量结构-爆轰性能关系(QSDR)模型,此外还应用标准SVM方法建立了QSDR模型,并用这2种模型进行呋咱系含能化合物密度的预测,随机选取85%化合物作为训练集,用来建立模型,其余化合物作为测试集来测试模型的预测能力。预测结果的交互检验的相关系数平方分别为0.9887和0.9885,平均相对误差分别为1.16%和2.12%,表明了2种建模方法的有效性。通过对2种模型的预测能力进行比较,GA-SVM方法建立的QSDR模型能更好地预测呋咱系含能化合物的密度,更利于实际应用。     

基于ICSVM的风电机组齿轮箱故障诊断

针对风电机组齿轮箱运行过程中故障样本缺乏、正常样本充裕的特点,提出基于增量代价敏感支持向量机（Incremental Cost-sensitive Support Vector Machine,ICSVM）的风电机组齿轮箱故障诊断方法。由于齿轮箱故障样本缺乏,建立以误分类代价最小化为目标的代价敏感支持向量机故障诊断模型;在增量训练代价敏感支持向量机阶段,利用KKT条件,以增量样本和初始样本训练增量代价敏感支持向量机。实验结果表明,该方法能有效地减少平均误分类代价和训练时间,提高齿轮箱故障识别率。     

基于聚类支持向量机的船用污水处理装置故障诊断

针对船用污水处理装置状态监测与故障诊断问题,提出了一种聚类支持向量机的故障诊断算法模型。该算法模型首先采用神经网络聚类算法将设备监测状态样本空间聚类分析出正常与异常子空间,再对异常子空间构造多分类支持向量机对故障进行诊断识别。该算法模型避免了盲目故障分类,提高了分类性能。通过对某船用污水处理装置实测样本的训练和检验表明,该算法具有较好的泛化性和推广能力。     

遗传优化支持向量机的软件可靠性预测模型

软件可靠性预测在软件开发的早期就能预测出哪些模块有出错倾向。提出一种改进的支持向量机来进行软件可靠性预测。针对支持向量机参数难选择的问题,将遗传算法引入到支持向量机的参数选择中,构造基于遗传算法优化支持向量机的软件可靠性预测模型,并用主成分分析的方法对软件度量数据进行降维,通过仿真实验,证明该模型比支持向量机、BP神经网络、分类回归树和聚类分析等预测模型具有更高的预测精度。     

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of improved particle swarm optimization, which can not only avoid the search being trapped in local optimum and but also help to search the optimum quickly by using chaos queues. The wireless sensor is employed as research object, and its four fault types including shock, biasing, short circuit and shifting are applied to test the diagnostic ability of CPSO-SVM compared with other diagnostic methods. The diagnostic results show that CPSO-SVM has higher diagnostic accuracy of wireless sensor than PSO-SVM and BP neural network.     

Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.     

基于几何算法的投影孪生支持向量机

投影孪生支持向量机（PTSVM）是最近提出的一种具有较好泛化性能的分类模型,但由于采用内点算法求解二次规划问题,PTSVM的训练速度较慢。针对该缺陷,提出一种快速的、基于几何算法的 PTSVM（GPTS-VM）。遵循 PTSVM的几何思想,提出一种新的二次规划模型,为每类数据产生一个投影方向;然后基于优化理论推导该模型的对偶问题并给予明确的几何解释,并利用计算几何算法求解。实验表明,提出的方法具有更快的训练速度和更好的泛化性能。     

基于支持向量机的图像型火灾探测算法

针对传统火灾探测方法存在的不足,提出了一种基于支持向量机的图像型火灾探测算法,并与基于神经网络的图像型火灾探测算法做了比较。实验结果表明支持向量机克服了神经网络容易过学习、容易陷入局部极小点等不足,同时避免了人为设定特征量识别阈值时需要做大量实验和统计的复杂性。基于支持向量机的图像型火灾探测算法识别准确率高,对于小样本、高维数、非线性的分类问题效果显著。     

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.     

Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts

In view of the dissatisfactory capability of the ε-insensitive loss function in field of white (Gaussian) noise of multi-dimensional load series, a new wavelet v-support vector machine with Gaussian loss function which is called Wg-SVM is put forward to penalize the Gaussian noises. To seek the optimal parameters of Wg-SVM, modified genetic algorithm (GA) is proposed to optimize parameters of Wg-SVM. The results of application in load forecasts show that the forecasting approach based on the Wg-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other SVM methods.     

Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer

Failures of power transformer are related with key-gas ratios C₂H₂/C₂H₄, CH₄/H₂ and C₂H₄/C₂H₆ strongly. Forecasting of these ratios of key-gas in power transformer oil is very significant to detect and identify incipient failures of transformer early. Forecasting of the ratios of key-gas in power transformer oil is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine with genetic algorithm (SVMG) is proposed to forecast the ratios of key-gas in power transformer oil, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The experimental results indicate that the SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. Consequently, the SVMG model is a proper alternative for forecasting of the ratios of key-gas in power transformer oil.     

基于支持向量机的旋转机械故障诊断

为了解决旋转机械故障的在线诊断识别问题，用小波包从旋转机械的震动信号中提取特征向量，给出了一种基于支持向量机的故障诊断分类方法。该方法通过有限的学习样本，建立旋转机械故障特征与其运行状态之间的关系。利用获得的矿井提升机减速箱齿轮数据建立了多级故障分类器，通过对样本的分类输出检验，验证了该故障诊断方法的可行性。     

基于梯度算法的支持向量机参数优化方法

首先介绍最近出现的参数优化方法,概括了高效率的参数优化算法应具备的若干特点.然后提出了一种新的支持向量机参数优化方法.该方法先在局域内用混沌优化搜索局域最优点,再将此最优点作为梯度方向,通过改变局域范围跳出局部寻优区域.该方法降低了对性能函数连续且可微的要求,收敛速度快,最终优化解与支持向量机的参数初始值无关.最后,通过仿真实验表明了该方法具有更高的分类和回归准确率.     

ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

We propose a systematic ECG quality classification method based on a kernel support vector machine（KSVM） and genetic algorithm（GA） to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function（GRBF） is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function（MHWF）. GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search（GS） method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive（TP）, false positive（FP）, and classification accuracy were used as the assessment indices. For training database set A（1000 recordings）, the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were： TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B（500 recordings）, the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.