基于核独立分量分析的模糊核聚类神经网络集成方法*

提出一种基于核独立分量分析的模糊核聚类神经网络集成方法。该方法首先采用核独立分量分析对高维数据进行特征提取;随后用模糊核聚类算法根据相互独立训练出的多个神经网络个体在验证集上的输出对其进行分类,并计算每一类中的所有个体在验证集上的泛化误差;然后取其中平均泛化误差最小的神经网络个体作为这一类的代表;最后经相对多数投票法得到集成的最终输出。实验结果表明,与其他集成方法相比,该方法具有较高的精确度和稳定性。     

基于动态粗糙集约简的选择性支持向量机集成

支持向量机集成是提高支持向量机泛化性能的有效手段,个体支持向量机的泛化能力及其之间的差异性是影响集成性能的关键因素。为了进一步提升支持向量机整体泛化性能,提出利用动态粗糙集的选择性支持向量机集成算法。首先在利用Boosting算法对样本进行扰动基础上,采用遗传算法改进的粗糙集与重采样技术相结合的动态约简算法进行特征扰动,获得稳定、泛化能力较强的属性约简集,继而生成差异性较大的个体学习器;然后利用模糊核聚类根据个体学习器在验证集上的泛化误差来选择最优个体;并用支持向量机算法对最优个体进行非线性集成。通过在UCI数据集进行仿真,结果表明算法能明显提高支持向量机的泛化性能,具有较低的时、空复杂性,是一种高效、稳定的集成方法。     

基于灰关联分析的KFCM-LSSVM蒸发过程软测量

针对氧化铝蒸发过程出口浓度在线检测问题,提出一种基于灰色关联分析和核模糊聚类相结合的多支持向量机蒸发过程建模方法.该方法采用灰色关联分析提取影响出口浓度较大的变量,再用核模糊聚类算法将样本划分成不同的子类,并对各子类构建的支持向量机模型的输出综合加权得到最终输出.对蒸发过程生产数据进行实验,并与核模糊聚类最小二乘支持向量机(KFCM-LSSVM)相比较,结果表明新模型鲁棒性较强、精度较高、泛化性更好,可以用于蒸发过程的优化控制.     

多类分类预选取的SVM在语音识别中的应用

支持向量机在训练过程中,将很多时间都浪费在对非支持向量的复杂计算上,特别是对于大规模数据量的语音识别系统来说,支持向量机在训练时间上不必要的开销将会更加显著。核模糊C均值聚类是一种常用的典型动态聚类算法,并且有核函数能够把模式空间的数据非线性映射到高维特征空间。在核模糊C均值聚类的基础上,结合了多类分类支持向量机中的一对一方法,按照既定的准则把训练样本集中有可能属于支持向量的样本数据进行预选取,并应用到语音识别中。实验取得了较好的结果,该方法有效地提高了支持向量机分类器的学习效率和泛化能力。     

基于核模糊聚类的多模型LSSVM回归建模

针对大规模数据采用单模型回归存在精度差和计算量较大的问题,提出一种基于核模糊聚类的多模型最小二乘支持向量回归建模方法.该方法首先使用基于条件正定核的模糊C均值聚类算法对数据集做出聚类划分;然后针对每个聚类做最小二乘支持向量回归估计;同时根据每个聚类内数据分布的特征,给出了一种简单的核参数选择方法.利用数值仿真实验进行非线性函数估计,实验结果表明了所提出的方法具有良好的精度和泛化能力.     

基于两阶段聚类的模糊支持向量机

为了提高模糊支持向量机在大数据集上的训练效率,提出一种基于两阶段聚类的模糊支持向量机算法。第1阶段为粗粒度聚类阶段,在每类训练样本上执行密度聚类算法,设置较大的邻域半径(给定邻域内最小点数),保证可能成为支持向量的样本点都被选取;第2阶段为自适应聚类阶段,在粗选的数据集合上,执行自适应密度聚类算法,根据各个点距离分类面的远近,自适应决定该点的邻域半径(给定邻域内最小点数)。这样可有效地减少远离分类面的聚类边缘点的数量,同时在分类面附近保持较多的样本点,试验结果表明,基于两阶段聚类模糊支持向量机算法,相比以往的方法,不仅提高了模糊支持向量机的训练效率,同时保持了较好的分类效果。     

基于模糊核聚类和SVM的说话人辨识

支持向量机作为一种新的统计学习方法,在说话人识别中得到了广泛应用.本文针对支持向量机在说话人辨识中的大样本训练耗时问题,提出对语音参数进行模糊核聚类的约简方法,选择聚类边界的语音参数作为支持向量,可以在不影响识别率的情况下,减少支持向量机的训练量.并通过实验验证了该方法的有效性.     

基于模糊核聚类和SVM的说话人辨识

支持向量机作为一种新的统计学习方法,在说话人识别中得到了广泛应用。本文针对支持向量机在说话人辨识中的大样本训练耗时问题,提出对语音参数进行模糊核聚类的约简方法,选择聚类边界的语音参数作为支持向量,可以在不影响识别率的情况下,减少支持向量机的训练量。并通过实验验证了该方法的有效性。     

基于双重扰动的选择性支持向量机集成

为了进一步提升支持向量机泛化性能,提出一种基于双重扰动的选择性支持向量机集成算法。利用Boosting方法对训练集进行扰动基础上,采用基于相对核的粗糙集相对约简与重采样技术相结合的动态约简算法进行特征扰动以生成个体成员,然后基于负相关学习理论构造遗传个体适应度函数,利用加速遗传算法选择权重大于阈值的最优个体进行加权集成。实验结果表明,该算法具有较高的泛化性能和较低的时、空复杂性,是一种高效的集成方法。     

一种基于聚类的核向量机参数C选择算法

核向量机可以高效学习大样本数据集,却有泛化能力低的缺陷.针对已有参数C选择算法缺乏启发性以及选取困难的不足,本文在分析了核聚类算法和距离比较算法的基础之上,提出基于核聚类的相对距离比较方法,该算法利用核聚类算法在特征空间对样本点进行聚类分簇,然后根据样本点到簇心相对距离的比值,得到参数C.本文在理论和实验两个方面,证明该算法有效地选择参数C,从而提高核支持向量机算法的泛化能力.     

A heuristic training-based least squares support vector machines for power system stabilization by SMES

This paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES.     

Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

The aim of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A – EEG signals recorded from healthy volunteers with eyes open and set E – EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive – AR, moving average – MA, least squares modified Yule–Walker autoregressive moving average – ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

一种基于改进混合蛙跳的KFCM算法

针对核模糊C-均值（KFCM）聚类算法存在易陷入局部极小值,对初始值敏感的缺点。将混合蛙跳算法（shuffled frog leaping algorithm,SFLA）用于KFCM中,但在聚类数较大和维数较高时,聚类效果不理想,为此提出将自适应惯性权重引入混合蛙跳算法的更新策略中,再用改进后的混合蛙跳算法求得最优解作为KFCM算法的初始聚类中心,利用KFCM算法优化初始聚类中心,求得全局最优解,从而有效克服了KFCM算法的缺点。人造数据和经典数据集的实验结果表明,新算法与KFCM和FCM聚类算法相比,寻优能力更强,迭代次数更少,聚类效果更好。     

A study on speaker identification based on weighted LS-SVM

As one of a practical method, least squares support vector machine (LS-SVM) is usable for nonlinear separable problem as speaker identification. However, single LS-SVM can only do such classifying as binary classification, so it always needs multiple LS-SVMs and corresponding algorithms for classifying multiple speakers in a speaker identification database. By comparing pairwise LS-SVM with one-against-all LS-SVM, it is obvious that the pairwise LS-SVM has the advantage of facilitative expanding for different cases, while the one-against-all LS-SVM can not bring. However conventional pairwise LS-SVM needs too many judgment times to do multi-classing. In order to improve the pairwise LS-SVM and make it applicable to multi-speaker identification system, we propose a new notion of classification weight for pairwise LS-SVM and the corresponding algorithm, named as pairwise LS-SVM based on classification weight, i.e., the m-ωLS-SVM method, which can be used in multi-speaker identification system. Experiment results show that, comparing with conventional pairwise LS-SVM, the identification speed of the system with m-ωLS-SVM method is improved while keeping correct rate of identification, or vice versa, with only a little increase of training time.     

最小二乘支持向量机在医疗数据分析中的应用

以医疗数据为应用对象,应用网格搜索和交叉验证的方法选择参数,建立最小二乘支持向量机分类器,进行实际验证,并与使用K近邻分类器(K-NN)和C4.5决策树两种方法的结果进行比较.结果表明,LS-SVM分类器取得较高的准确率,表明最小二乘支持向量机在医疗诊断研究中具有很大的应用潜力.     

SMO-based pruning methods for sparse least squares support vector machines

Solutions of least squares support vector machines (LS-SVMs) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS-SVM. In this paper, we propose a new pruning algorithm for sparse LS-SVMs: the sequential minimal optimization (SMO) method is introduced into pruning process; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.     

RRS + LS-SVM: a new strategy for “a priori” sample selection

We present in this work a new Sparse Hybrid Classifier, by using reduced remaining subset (RRS) with least squares support vector machine (LS-SVM). RRS is a sample selection technique based on a modified nearest neighbor rule. It is used in order to choose the best samples to represent each class of a given database. After that, LS-SVM uses the samples selected by RRS as support vectors to find the decision surface between the classes, by solving a system of linear equations. This hybrid classifier is considered as a sparse one because it is able to detect support vectors, what is not possible when using LS-SVM separately. Some experiments are presented to compare the proposed approach with two existent methods that also aim to impose sparseness in LS-SVMs, called LS ²-SVM and Ada-Pinv.     

基于LS-SVM的位置指纹室内定位

基于无线接入点（Access Point,AP）接收信号强度（Received Signal Strength,RSS）的位置指纹室内定位技术近几年已经成为国内外位置感知研究的热点。提出了基于最小二乘支持向量机（Least Squares Support Vector Machines,LS-SVM）的位置指纹定位方法。给出了基于LS-SVM的指纹定位模型,描述了LS-SVM指纹样本训练的具体实现过程。重点在于将定位问题转化为一个多类别分类问题,并分别采用一对一（OAO）和一对多（OAA）方法将其转化为多个二值分类问题。仿真结果表明,LS-SVM较传统支持向量机（SVMs）、K近邻（k-Nearest Neighbors,K-NN）定位方法的分类准确率高且计算代价小,平均分类准确率达92.00%。