基于FCM-PNN分类器的说话人识别

说话人识别的本质就是模式分类。传统分类器算法中参数模型方法的主要缺点是预先假定的概率分布函数形式不一定符合待分类的数据。非参数模型方法,如PNN分类器,可以有效地克服参数模型的缺点,但其巨大的内存开销与低的分类速度使得PNN作为大量和高维的数据样本分类几乎不可行。FCM虽具有良好的模糊聚类能力,但无法直接给出概率分类结果。该文提出的FCM-PNN分类器,在FCM聚类的基础上,以贝叶斯置信度为基础,利用PNN进行概率分类。它结合了FCM聚类和PNN概率分类的优势,同时克服了传统参数模型分类和FCM聚类的局限性。实验结果证实了FCM-PNN分类器具有分类精度高、速度快及揭示细节的能力。     

基于PNN的机载设备故障预测

故障预测是PHM体系的重要特征,提出基于过程神经网络(PNN)对机载设备进行故障预测;首先建立了基于PNN的多步预测模型,推导了模型在函数正交基展开形式下的计算公式;基于正交基函数展开和矢量矩免疫算法训练PNN,推导出了适用于进化算法的优化问题模型;以设备转换部件Out2信号的全寿命故障规律为例进行了故障预测,基于MATLAB进行了仿真,结果表明,基于PNN进行故障预测精度较高,是一种有效的方法。     

对适应值共享遗传算法的分类及评价

针对多峰函数优化问题先后出现了一系列适应值共享类的遗传算法.这些算法都需要事先提供某种信息.本文基于事先提供信息的区别提出了一种新的适应值共享类遗传算法的分类方法,并通过一个复杂的标准测试问题对这些算法进行了比较和评价,结果表明在各种算法中,清除算法、动态小生境共享算法和新聚类适应值共享算法具有较高的搜索能力和优化速度.本文的工作对于这些适应值共享类遗传算法的应用和进一步改进具有指导意义.     

一种基于混沌遗传与粒子群混合优化的过程神经网络训练算法

针对时变输入/输出过程神经网络的训练问题,提出一种基于混沌遗传与带有动态惯性因子的粒子群优化相结合的学习方法。综合利用粒子群算法的经验记忆、信息共享和混沌遗传算法的混沌轨道遍历搜索性质,基于PNN训练目标函数,构建两种算法相混合的进化寻优机制,通过适应度评估和优化效率分析自适应调节混沌遗传与粒子群算法的切换,实现网络参数在可行解空间的全局优化求解。实验结果表明,该算法较大提高了PNN的训练效率。     

基于距离和密度聚类的RBF网络结构设计

为确定径向基函数RBF(radial basis function)神经网络隐含层结构,并针对基于距离或密度聚类的RBF神经网络的限制,提出一种基于距离和密度聚类(GDD)算法的RBF神经网络.GDD算法通过计算每个样本的密度,各样本间的距离及相似条件(密度标准、距离标准),相似条件是根据样本分布而改变的,进行样本空间...     

事件检测支持向量机模型与神经网络模型比较

针对交通领域中的事件检测(无事件模式和有事件模式)模式识别问题,描述了支持向量机(SVM)的基本方法,建立了基于线性(linearfunction)、多项式(polynomialfunction)和径向基(radialbasisfunction)3种核函数的事件检测SVM模型,并与PNN、MLF模型进行了理论比较。采用I-880线圈数据集和事件数据集建立并验证SVM、PNN和MLF模型,结果发现:无论对于向北、向南或混合方向的事件检测,SVM模型的检测率(DR)、误报率(FAR)和平均检测时间(MTTD)指标均比MLF模型好;PNN模型的DR比SVM(P)模型的高,但FAR和MTTD指标不比SVM(P)模型好;在3个SVM模型中,SVM(P)检测效果最好,SVM(L)最差。SVM算法与神经网络算法相比具有避免局部最小,实现全局最优化,更好的泛化效果的优点,是高速公路事件检测的一种很有潜力的算法。     

基于快速密度聚类的RBF神经网络设计

针对径向基函数（radial basis function,RBF）神经网络隐含层结构难以确定的问题,提出一种基于快速密度聚类的网络结构设计算法。该算法将快速密度聚类算法良好的聚类特性用于RBF神经网络结构设计中,通过寻找密度最大的点并将其作为隐含层神经元,进而确定隐含层神经元个数和初始参数;同时,引入高斯函数的特性,保证了每个隐含层神经元的活性;最后,用一种改进的二阶算法对神经网络进行训练,提高了神经网络的收敛速度和泛化能力。利用典型非线性函数逼近和非线性动态系统辨识实验进行仿真验证,结果表明,基于快速密度聚类设计的RBF神经网络具有紧凑的网络结构、快速的学习能力和良好的泛化能力。     

基于改进PNN模型的人体健康监测方法

远程人体健康监测分析呈现滞后性、不准确性、设备昂贵等特点,因此难以实现实时、准确的人体健康监测分析。通过对人体健康监测方法和概率神经网络(PNN)的研究,将具有调节参数少、收敛速度快和保证获得贝叶斯最优解等优点的PNN应用于人体健康监测。但是PNN的缺点是未考虑不同类别模型之间的重叠和交错,以及当训练样本不满足假定条件时无法确定是否存在相应的PNN模型。针对这两个缺点,分析了径向基神经网络(RBNN)和广义回归神经网络(GRNN)的网络拓扑结构和优势,创新性地提出在PNN结构的模式层中引入RBNN结构,以及在PNN结构的输出层中引入GRNN结构,得到了一种新的径向基-广义回归-概率混合神经网络(RBF-GR-PMNN),从而满足实时、准确监测人体健康状况的要求。进行了RBF-GR-PMNN与一般PNN的对比试验。试验分别从准确率和运行时间等方面进行对比分析。试验结果证明了改进PNN在这些方面均优于一般PNN,进一步表明了RBF-GR-PMNN模型的有效性。     

改进的RBF网络及其参数优化方法

该文提出了一个改进的RBF网络及其参数优化方法。将典型的三层RBF网络改为一个两层RBF和一个单层感知器的串联网络。参数优化方法自动确定核函数个数,并根据核函数输出误差用BP算法修正核函数中心和宽度。根据样本分布的不规则性,引入了子类的概念,使每个类由若干子类覆盖,每个类生成一个单独的网络。实验表明,这种方法能得到较优的网络结构及其参数,并且提高了RBF网络中BP算法的收敛速度。     

一种量子衍生神经网络模型

为提高神经网络的逼近能力,通过在普通BP网络中引入量子旋转门,提出了一种新颖的量子衍生神经网络模型. 该模型隐层由量子神经元组成,每个量子神经元携带一组量子旋转门,用于更新隐层的量子权值,输入层和输出层均为普通神经元. 基于误差反传播算法设计了该模型的学习算法. 模式识别和函数逼近的实验结果验证了提出模型及算法的有效性.     

Shared kernel models for class conditional density estimation

We present probabilistic models which are suitable for class conditional density estimation and can be regarded as shared kernel models where sharing means that each kernel may contribute to the estimation of the conditional densities of an classes. We first propose a model that constitutes an adaptation of the classical radial basis function (RBF) network (with full sharing of kernels among classes) where the outputs represent class conditional densities. In the opposite direction is the approach of separate mixtures model where the density of each class is estimated using a separate mixture density (no sharing of kernels among classes). We present a general model that allows for the expression of intermediate cases where the degree of kernel sharing can be specified through an extra model parameter. This general model encompasses both the above mentioned models as special cases. In all proposed models the training process is treated as a maximum likelihood problem and expectation-maximization algorithms have been derived for adjusting the model parameters.     

Linear combination of densities and its direct estimation framework with applications

In this paper, typical learning task including data condensation, binary classification, identification of the independence between random variables and conditional density estimation is described from a unified perspective of a linear combination of densities, and accordingly a direct estimation framework based on a linear combination of Gaussian components (i.e., Gaussian basis functions) under integrated square error criterion is proposed to solve these learning tasks. The proposed direct estimation framework has three advantages. Firstly, different from most of the existing state-of-the-art methods in which estimating each component’s density in this linear combination of densities and then combining them linearly are required, it can directly estimate the linear combination of densities as a whole, and it has at least comparable to or even better approximation accuracy than the existing density estimation methods. Secondly, the time complexity of the proposed direct estimation framework is O(l ³) in which l is the number of Gaussian components in this framework which are generally viewed as the Gaussian distributions of the clusters in a dataset, and hence l is generally much less than the size of the dataset, so it is very suitable for large datasets. Thirdly, this proposed framework can be typically used to develop alternative approaches to classification, data condensation, identification of the independence between random variables, conditional density estimation and the similarity identification between multiple source domains and a target domain. Our preliminary results about experiments on several typical applications indicate the power of the proposed direct estimation framework.     

A modified probabilistic neural network for partial volume segmentation in brain MR image.

A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated.     

Semi-supervised kernel density estimation for video annotation

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in large-scale video annotation due to several difficulties, such as large variation of video content and intractable computational cost. In this paper, we propose a novel semi-supervised learning algorithm named semi-supervised kernel density estimation (SSKDE) which is developed based on kernel density estimation (KDE) approach. While only labeled data are utilized in classical KDE, in SSKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. It is a non-parametric method, and it thus naturally avoids the model assumption problem that exists in many parametric semi-supervised methods. Meanwhile, it can be implemented with an efficient iterative solution process. So, this method is appropriate for video annotation. Furthermore, motivated by existing adaptive KDE approach, we propose an improved algorithm named semi-supervised adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels rather than a fixed kernel, such that broader kernels can be applied in the regions with low density. In this way, more accurate density estimates can be obtained. Extensive experiments have demonstrated the effectiveness of the proposed methods.     

On the generalization ability of neural network classifiers

This correspondence presents a method for evaluation of artificial neural network (ANN) classifiers. In order to find the performance of the network over all possible input ranges, a probabilistic input model is defined. The expected error of the output over this input range is taken as a measure of generalization ability. Two essential elements for carrying out the proposed evaluation technique are estimation of the input probability density and numerical integration. A nonparametric method, which depends on the nearest M neighbors, is used to locally estimate the distribution around each training pattern. An orthogonalization procedure is utilized to determine the covariance matrices of local densities. A Monte Carlo method is used to perform the numerical integration. The proposed evaluation technique has been used to investigate the generalization ability of back propagation (BP), radial basis function (RBF) and probabilistic neural network (PNN) classifiers for three test problems     

一种自适应在线核密度估计方法

给定一组观察数据,估计其潜在的概率密度函数是统计学中的一项基本任务,被称为密度估计问题.随着数据收集技术的发展,出现了大量的实时流式数据,其特点是数据量大,数据产生速度快,并且数据的潜在分布也可能随着时间而发生变化,对这类数据分布的估计也成为亟待解决的问题.然而,在传统的密度估计算法中,参数式算法因为有较强的模型假设导致其表达能力有限,非参数式算法虽然具有更好的表达能力,但其计算复杂度通常很高.因此,它们都无法很好地应用于这种流式数据的场景.通过分析基于竞争学习的学习过程,提出了一种在线密度估计算法来完成流式数据上的密度估计任务,并且分析了其与高斯混合模型之间的密切联系.最后,将所提算法与现有的密度估计算法进行对比实验.实验结果表明,与现有的在线密度估计算法相比,所提算法能够取得更好的估计结果,并且能够基本上达到当前最好的离线密度估计算法的估计性能.     

Canonical locality preserving Latent Variable Model for discriminative pose inference

Discriminative approaches for human pose estimation model the functional mapping, or conditional distribution, between image features and 3D poses. Learning such multi-modal models in high dimensional spaces, however, is challenging with limited training data; often resulting in over-fitting and poor generalization. To address these issues Latent Variable Models (LVMs) have been introduced. Shared LVMs learn a low dimensional representation of common causes that give rise to both the image features and the 3D pose. Discovering the shared manifold structure can, in itself, however, be challenging. In addition, shared LVM models are often non-parametric, requiring the model representation to be a function of the training set size. We present a parametric framework that addresses these shortcomings. In particular, we jointly learn latent spaces for both image features and 3D poses by maximizing the non-linear dependencies in the projected latent space, while preserving local structure in the original space; we then learn a multi-modal conditional density between these two low-dimensional spaces in the form of Gaussian Mixture Regression. With this model we can address the issue of over-fitting and generalization, since the data is denser in the learned latent space, as well as avoid the need for learning a shared manifold for the data. We quantitatively compare the performance of the proposed method to several state-of-the-art alternatives, and show that our method gives a competitive performance.     

基于最小联合互信息亏损的最优特征选择算法

提出了一种基于最小联合互信息亏损的最优特征选择算法。该算法首先通过一种动态渐增策略搜索一个特征全集的无差异特征子集,并基于最小条件互信息原则在保证每一步中联合互信息量亏损都最小的情况下筛选其中的冗余特征,从而得到一个近似最优特征子集。针对现有基于条件互信息的条件独立性测试方法在高维特征域上所面临的效率瓶颈问题,给出了一种用于估计条件互信息的快速实现方法,并将其用于所提算法的实现。分类实验结果表明,所提算法优于经典的特征选择算法。此外,执行效率实验结果表明,所提条件互信息的快速实现方法在执行效率上有着显著的优势。     

On classification with incomplete data

We address the incomplete-data problem in which feature vectors to be classified are missing data (features). A (supervised) logistic regression algorithm for the classification of incomplete data is developed. Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the observed data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both expectation-maximization (EM) and variational Bayesian EM (VB-EM). The proposed supervised algorithm is then extended to the semisupervised case by incorporating graph-based regularization. The semisupervised algorithm utilizes all available data-both incomplete and complete, as well as labeled and unlabeled. Experimental results of the proposed classification algorithms are shown     

后向散射系数的贝叶斯估计

后向散射系数是合成孔径雷达图像中重要的物理参数.由于合成孔径雷达测量系统的噪声干扰和其他不确定因素影响使得测量数据往往不够精确,这就需要对测量数据进行合理估计.为了对后向散射系数做出准确合理的估计,文章将后向散射系数的先验知识考虑进去,给出了后向散射系数的三种贝叶斯估计算法.贝叶斯估计的关键是概率密度模型的选取.例中选用贝塔(Beta)分布作为先验概率密度模型,伽玛(Gamma)分布作为条件概率密度模型得到了合理的估计结果,并与最大似然估计(ML)算法进行了比较,比较结果表明在对后向散射系数的估计中,贝叶斯估计算法要明显优于最大似然估计算法.