基于融合特征与支持向量机的控制图模式识别

为了提高控制图模式识别的精度, 将控制图模式的原始特征与形状特征相融合得到分类特征, 并采用支持向量机进行模式分类的控制图模式识别。融合所得特征既保持了控制图模式的原始特征所蕴涵的模式全局特性信息, 又通过引入形状特征对部分易混淆模式的局部几何特性进行强化, 使不同模式间的区分度得到有效提高; 而以支持向量机作为模式分类器保证方法在高维度特征和小样本条件下也能获得较好的识别性能。仿真实验结果表明所提方法的识别精度相比其他几种基于形状特征的控制图模式识别方法有明显提高。     

Recognition of control chart patterns using an intelligent technique

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.     

Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.     

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.     

基于遗传优化的PCA-SVM控制图模式识别

针对SVM和PCA-SVM进行质量控制图模式识别时泛化能力不足和识别精度不高的问题,提出一种基于遗传优化的PCA-SVM控制图模式识别方法。该方法的基本思想是首先基于特征子空间降维方法,运用PCA算法对原始特征样本进行主元分析,有效降低原始特征样本维数并突出聚类,提取各模式之间的主元特征;然后把此特征看成遗传算法中一组染色体,对支持向量机分类器核参数和惩罚因子进行二进制编码,通过对随机产生的一组染色体进行模式识别,并将此识别率作为遗传算法的适应度函数,通过选择、交叉和变异操作,对其参数进行自适应寻优;最后用优化的支持向量机分类器进行控制图模式识别。通过仿真进行验证,结果显示基于遗传优化的PCA-SVM分类器模型的控制图模式泛化能力强、识别精度高,可适用于生产现场质量控制。     

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.     

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.     

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.     

Using support vector machine with a hybrid feature selection method to the stock trend prediction

In this paper, we developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. This proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set. To evaluate the prediction accuracy of this SVM-based model combined with F_SSFS, we compare its performance with back-propagation neural network (BPNN) along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty, and Correlation-based feature selection via paired t-test. The grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In this study, we show that SVM outperforms BPN to the problem of stock trend prediction. In addition, our experimental results show that the proposed SVM-based model combined with F_SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that SVM combined with F_SSFS can serve as a promising addition to the existing stock trend prediction methods.     

基于支持向量机的控制图模式识别

为了提高控制图模式识别效果，提出混合核函数支持向量机的模式识别方法。在模型构造中采用一对一多类分类支持向量机，并利用遗传算法优化混合核函数支持向量机参数。仿真和应用结果表明，混合核函数支持向量机对各种模式控制图的总体识别率，I型错判均优于单独核函数、概率神经网络和小波概率神经网络，且具有良好的泛化能力，适合生产现场实时在线工序质量控制。     

一种基于GA和支持向量机的车牌字符识别方法

以高斯核为其核函数的支持向量机在实际应用中表现出优良的学习性能,被广泛应用于模式分类中。支持向量机的识别性能对参数的选取是敏感的,惩罚因子C和核函数参数σ对支持向量机性能会产生重要的影响。针对高斯核支持向量机在车牌字符识别问题中的应用,提出了一种基于遗传算法的参数选择方法。首先确定合适的遗传算法适应度函数,然后利用遗传算法对支持向量机的参数进行优化,最后在各个识别子网中分别采用参数优化后的支持向量机对车牌字符进行识别。实验结果表明,该方法取得了令人满意的识别率。     

A hybrid learning-based model for on-line detection and analysis of control chart patterns

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Therefore, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. In recent years, neural networks (NNs) have been successfully used to the CCP recognition task. The emphasis has been on the CCP detection rather than more detailed quantification of information of the CCP. Additionally, a common problem in existing NN-based CCP recognition methods is that of discriminating between various types of CCP that share similar features in a real-time recognition scheme. This work presents a hybrid learning-based model, which integrates NN and DT learning techniques, to detect and discriminate typical unnatural CCPs, while identifying the major parameter (such as the shift displacement or trend slope) and starting point of the CCP detected. The performance of the model was evaluated by simulation, and numerical and graphical results that demonstrate that the proposed model performs effectively and efficiently in on-line CCP recognition task are provided. Although this work considers the specific application of a real-time CCP recognition model for the individuals (X) chart, the proposed learning-based methodology can be applied to other control charts (such as the X-bar chart).     

Speaker recognition using pyramid match kernel based support vector machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel, GMM-UBM mean interval kernel (GUMI) and intermediate matching kernel. Recently, the pyramid match kernel (PMK) using grids in the feature space as histogram bins and vocabulary-guided PMK (VGPMK) using clusters in the feature space as histogram bins have been proposed for recognition of objects in an image represented as a set of local feature vectors. In PMK, a set of feature vectors is mapped onto a multi-resolution histogram pyramid. The kernel is computed between a pair of examples by comparing the pyramids using a weighted histogram intersection function at each level of pyramid. We propose to use the PMK-based SVM classifier for speaker identification and verification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the PMK-based SVM classifier is construction of a pyramid of histograms. We first propose to form hard clusters, using k-means clustering method, with increasing number of clusters at different levels of pyramid to design the codebook-based PMK (CBPMK). Then we propose the GMM-based PMK (GMMPMK) that uses soft clustering. We compare the performance of the GMM-based approaches, and the PMK and other dynamic kernel SVM-based approaches to speaker identification and verification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification and verification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the state-of-the-art GMM-based approaches. For speaker recognition task, the GMMPMK-based SVM gives a performance that is better than that of SVMs using many other dynamic kernels and comparable to that of SVMs using state-of-the-art dynamic kernel, GUMI kernel. The storage requirements of the GMMPMK-based SVMs are less than that of SVMs using any other dynamic kernel.     

基于SVM的入侵检测系统

支持向量机（SVM）作为一种新型的统计学习模型，在处理小样本和学习机的推广能力上具有很大的优势。该文应用SVM的分类特性来识别网络攻山行为，提出了基于SVM的入侵检测方法。雨点考察了不同SVM核函数和参数选择对检测准确率和实时性的影响。论证了基于SVM的入侵检测在性能和识别率上都明显优于基于BP网络的攻击识别，还就目前商用入侵检测系统存在较高误报率的问题，分析了用SVM来提高其检测实时性和识别准确率的系统框架。     

Fuzzy Regression Analysis by Support Vector Learning Approach

Support vector machines (SVMs) have been very successful in pattern classification and function approximation problems for crisp data. In this paper, we incorporate the concept of fuzzy set theory into the support vector regression machine. The parameters to be estimated in the SVM regression, such as the components within the weight vector and the bias term, are set to be the fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model and has been attempted to treat fuzzy nonlinear regression analysis. In contrast to previous fuzzy nonlinear regression models, the proposed algorithm is a model-free method in the sense that we do not have to assume the underlying model function. By using different kernel functions, we can construct different learning machines with arbitrary types of nonlinear regression functions. Moreover, the proposed method can achieve automatic accuracy control in the fuzzy regression analysis task. The upper bound on number of errors is controlled by the user-predefined parameters. Experimental results are then presented that indicate the performance of the proposed approach.     

An hybrid detection system of control chart patterns using cascaded SVM and neural network�Cbased detector

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.     

基于K均值聚类和粒子群优化的多核SVM图像分割

图像分割是图像理解和计算机视觉的重要内容.针对单核SVM在进行图像分割过程中不能兼顾分割精度高和泛化性能好的问题,提出一种基于K均值聚类和优化多核SVM的图像分割算法.该算法首先运用K均值聚类算法自动选取训练样本,然后提取其颜色特征和纹理特征作为训练样本的特征属性,并使用其对构造的多核SVM分割模型进行训练,最后用粒子群优化算法对多核核参数、惩罚因子以及核权重系数联合寻优,使生成的多核SVM具有更好的分割性能.实验结果表明,本文方法在有效提取图像目标细节的同时,获得了更高的分割精度,与基于单核的SVM分割模型相比,具有更强的泛化能力.     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

核对齐多核模糊支持向量机

支持向量机（SVMs）是当前被广泛使用的机器学习技术,其通过最优分割超平面来提高分类器的泛化能力,在实际应用中表现优异。然而SVM也存在易受噪声影响,以及核函数选择等难题。针对以上问题,本文将基于核对齐的多核学习方法引入到模糊支持向量机（fuzzy support vector machine, FSVM）中,提出了模糊多核支持向量机模型（multiple kernel fuzzy support vector machine,MFSVM）。MFSVM通过模糊粗糙集方法计算每一样例隶属度;其次,利用核对齐的多核方法计算每一单核权重,并将组合核引入到模糊支持向量机中。该方法不仅提高了支持向量机的抗噪声能力,也有效避免了核选择难题。在UCI数据库上进行实验,结果表明本文所提方法具有较高的分类精度,验证了该方法的可行性与有效性。     

Gaussian小波SVM及其混沌时间序列预测

为了提高混沌时间序列的预测精度,针对小波有利于信号细微特征提取的优点,结合小波技术和SVM的核函数方法,提出基于Gaussian小波SVM的混沌时间序列预测模型.证明了偶数阶Ganssian小波函数满足SVM平移不变核条件,并构建相应的Gaussian小波SVM.时混沌时间序列进行相空间重构,将重构相空间中的向量作为SVM的输入参量.用Ganssian小波SVM与常用的径向基SVM及Morlet小渡SVM进行对比实验,通过对Chen's混沌时间序列和负荷混沌时间序列的预测,结果表明,Ganssian小波SVM的效果比其他两种SVM更好.