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。     

基于概率神经网络的高速公路事件检测

针对高速公路事件检测这一非线性分类问题,提出一种基于概率神经网络的事件检测方法。阐述了概率神经网络的结构与训练算法,分析了事件对交通流的影响规律,并合理地选取了概率神经网络的输入量,用高速公路管理部门提供的样本数据进行了仿真研究。仿真实验表明,基于概率神经网络的事件检测方法具有学习速度快、泛化能力好、检测准确率高等优点,具有良好的应用前景。     

BA-PNN-based methods for power transformer fault diagnosis

This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis.     

高光谱数据分类新方法研究

传统的独立分量分析（ICA）算法无法确定高光谱数据中独立分量的个数,利用概率神经网络（PNN）训练时间短的优点,根据分类精度可以较快地确定出独立分量的个数。提出了一种在确定高光谱数据的维数之后利用支持向量机（SVM）分类的新算法思想,首先利用ICA对高光谱数据降维,并利用PNN确定出独立分量的个数,而后对降维后的数据利用SVM作交叉验证,并采用混合核函数进行分类的算法思想。通过仿真实验表明,该算法可以在保证分类精度的同时大大减少分类的时间。     

基于LMS-PNN算法在心音识别与预测中的应用

传统的概率神经网络（Probability neural network, PNN）具有很强的容错性、学习过程简单、训练速度快等特点。为提高传统PNN在心音分类方面的性能,利用最小均方(Least mean square, LMS)方法对其进行优化,进而提高心音分类与预测的准确性。LMS-PNN算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS方法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN,抽取40 000个样本数据进行训练,并对各心音进行等级划分与预测。从PNN的模式层输入训练数据后,由实验数据验证可知,LMS-PNN算法的预测准确率可达96%以上。     

Temporal updating scheme for probabilistic neural network withapplication to satellite cloud classification

In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual information and adjusting the PNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the PNN updated up to the last frame while at the same time, a prediction using Markov chain models is also made based on the classification results of the previous frame. The results of both the old PNN and the predictor are then compared. Depending on the outcome, either a supervised or an unsupervised updating scheme is used to update the PNN classifier. Maximum likelihood (ML) criterion is adopted in both the training and updating schemes. The proposed scheme is examined on both a simulated data set and the Geostationary Operational Environmental Satellite (GOES) 8 satellite cloud imagery data. These results indicate the improvements in the classification accuracy when the proposed scheme is used.     

软组合概率神经网络分类器人脸识别方法

概率神经网络分类器具有学习速度快、易于实现的特点,而且其输出是后验概率, 使得分类器的软组合变得容易。利用概率神经网络的这些特点,提出了软组合概率神经网络分类器人脸识别方法,该方法包括3步:(1)对人脸图像做不完全小波包分解;(2)用包含低频成分的小波子空间图像训练概率神经网络分类器;(3)用模糊积分组合训练好的分类器。将该方法与3种基于矩阵子空间的人脸识别方法在JAFFE、YALE、ORL和FERET 4个人脸数据库上进行了实验比较,结果表明,提出的方法在识别精度和CPU时间两方面均优于其他3种方法。     

Comparison of two different PNN training approaches for satellitecloud data classification

Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.     

基于SOM-PNN分类器的体数据概率分类及绘制

概率分类是三维医学体数据绘制必不可少的预处理环节。本文提出的ＳＯＭ－ＰＮＮ分类器，以贝叶斯置信度为基础，给出概率分类结果，并用于三维体制制，得到了良好的图像质量和较高的分类效率。     

基于LMS-PNN神经网络算法在心音识别与预测中的应用

传统的PNN神经网络具有很强的容错性、学习过程简单、训练速度快等特点,本文在传统PNN神经网络的基础上,利用LMS对其在心音分类方面进行优化,进而提高心音分类与预测的准确性。LMS-PNN神经网络算法对心音的信号运用窗函数进行分帧,利用双门限法确定数据的值,运用LMS算法对相应的参数进行调试,并将去噪后的数据以mat格式保存,提取出各个心音的短时自相关系数以及短时功率谱密度,并运用PNN神经网络,抽取40000个样本数据进行训练,并将各个心音进行等级划分与预测。 从PNN神经网络的模式层输入训练数据后,通过仿真测试可得,LMS—PNN神经网络预测准确率可达可达96%以上。     

一种新的用于说话人辨认的PNN分类器的研究

给出了一种新的类条件密度函数估计的σPNN模型,它基于模式层共享的PNN和模式 层分离的PNN,即每个类不仅拥有一组只属于自己的模式层,还拥有所有类都共享的几个模式 层,这里共享意味着每个核函数对所有类的条件密度估计都有贡献,新模型的训练采用最大似然 准则,并改进了EM算法来调整模型参数.闭集文本自由说话人辨认试验证明了提出的模型及其 算法的正确性．     

Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure

In classical feedforward neural networks such as multilayer perceptron, radial basis function network, or counter‐propagation network, the neurons in the input layer correspond to features of the training patterns. The number of these features may be large, and their meaningfulness can be various. Therefore, the selection of appropriate input neurons should be regarded. The aim of this paper is to present a complete step‐by‐step algorithm for determining the significance of particular input neurons of the probabilistic neural network (PNN). It is based on the sensitivity analysis procedure applied to a trained PNN. The proposed algorithm is utilized in the task of reduction of the input layer of the considered network, which is achieved by removing appropriately indicated features from the data set. For comparison purposes, the PNN's input neuron significance is established by using the ReliefF and variable importance procedures that provide the relevance of the input features in the data set. The performance of the reduced PNN is verified against a full structure network in classification problems using real benchmark data sets from an available machine learning repository. The achieved results are also referred to the ones attained by entropy‐based algorithms. The prediction ability expressed in terms of misclassifications is obtained by means of a 10‐fold cross‐validation procedure. Received outcomes point out interesting properties of the proposed algorithm. It is shown that the efficiency determined by all tested reduction methods is comparable.     

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

The aim of this work is to develop an unsupervised approach based on Probabilistic Neural Network (PNN) for land use classification. A time series of high spatial resolution acquired by LANDSAT and SPOT images has been used to firstly generate the profiles of Normalized Difference Vegetation Index (NDVI) and then used for the classification procedure.The proposed method allows the implementation of cluster validity technique in PNN using Ward's method to get clusters. This procedure is completely automatic with no parameter adjusting and instantaneous training, has high ability in producing a good cluster number estimates and provides a new point of view to use PNN as unsupervised classifier. The obtained results showed that this approach gives an accurate classification with about 3.44% of error through a comparison with the real land use and provides a better performance when comparing to usual unsupervised classification methods (fuzzy c-means (FCM) and K-means).     

基于果蝇算法优化的概率神经网络在变压器故障诊断中的应用

传统的变压器故障诊断方法存在编码不全,容易错判漏判的缺点。随着变压器在线监测技术的发展和产品需求的增加,变压器故障诊断技术朝着智能化的方向发展。为提高故障诊断率,结合油中气体分析法,本文提出了一种基于果蝇算法优化的概率神经网络模型的变压器故障诊断方法。作为一种新型的启发式和进化式算法,果蝇优化算法具有易理解和快速收敛到全局最优解的优点。概率神经网络结构简单、训练简洁,具有强大的非线性分类能力,将样本空间映射到故障模式空间中,从而形成一有较强容错能力和机构自适应能力的诊断网络。采用果蝇算法对模型参数进行优化,减少人为因素对神经网络设计的影响。仿真实验证明这种基于果蝇优化算法的概率神经网络可以有效地运用到变压器故障诊断中,为变压器故障诊断供了一条新途径,具有良好的研究价值和发展前景。     

基于模糊自适应共振理论映射算法的单样本三维人脸识别

针对传统的三维人脸识别分类算法大多需要多个样本进行训练,而在单训练样本的前提下识别性能会严重降低的问题,提出了基于模糊自适应共振理论映射(Fuzzy ARTMAP)的算法对三维人脸数据库进行分类识别。首先对三维人脸深度图像进行局部二值模式(LBP)统一模式算子的特征提取,再对LBP特征进行Log-Gabor小波变换,提取图像的频域特征向量作为训练的输入向量,最后将单样本训练向量集送入Fuzzy ARTMAP分类器进行训练识别。该算法在FRGC v2.0三维人脸数据库中的识别率可达到87.15%,分类器的训练时间为24.88s,单张待识别人脸样本与单张已注册的人脸匹配时间为0.0015s,一张新的人脸样本在数据库完成一次搜索匹配则需要1.08s。实验结果表明,所提方法在测试中的性能优于概率神经网络(PNN)和极限学习机神经网络(ELM),既能保证较高的识别率,又能拥有较短的训练时间,且时间增幅稳定,可控性强。     

Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification

In this article, an iterative procedure is proposed for the training process of the probabilistic neural network (PNN). In each stage of this procedure, the Q(0)-learning algorithm is utilized for the adaptation of PNN smoothing parameter (σ). Four classes of PNN models are regarded in this study. In the case of the first, simplest model, the smoothing parameter takes the form of a scalar; for the second model, σ is a vector whose elements are computed with respect to the class index; the third considered model has the smoothing parameter vector for which all components are determined depending on each input attribute; finally, the last and the most complex of the analyzed networks, uses the matrix of smoothing parameters where each element is dependent on both class and input feature index. The main idea of the presented approach is based on the appropriate update of the smoothing parameter values according to the Q(0)-learning algorithm. The proposed procedure is verified on six repository data sets. The prediction ability of the algorithm is assessed by computing the test accuracy on 10 %, 20 %, 30 %, and 40 % of examples drawn randomly from each input data set. The results are compared with the test accuracy obtained by PNN trained using the conjugate gradient procedure, support vector machine algorithm, gene expression programming classifier, k–Means method, multilayer perceptron, radial basis function neural network and learning vector quantization neural network. It is shown that the presented procedure can be applied to the automatic adaptation of the smoothing parameter of each of the considered PNN models and that this is an alternative training method. PNN trained by the Q(0)-learning based approach constitutes a classifier which can be treated as one of the top models in data classification problems.     

基于小波倒谱系数和概率神经网络的取证说话人识别模型

取证说话人识别是一种利用犯罪现场留下的质疑语音来识别犯罪分子身份的技术。为了提高识别模型的抗噪能力,本论文提出了基于小波倒谱系数（WCC）和概率神经网络（PNN）的取证说话人识别模型。该模型包含WCC特征提取和PNN分类两个步骤。WCC对噪音不敏感,所以使得我们的模型有抗噪能力。PNN是一种高效的分类算法,从而提高了模型识别性能。实验表明,该模型以提高时间消耗为代价提高了识别率和抗噪能力。     

An expert model for self-care problems classification using probabilistic neural network and feature selection approach

Self-care problems classification is one of the important challenges for occupational therapists. Extent and variety of disorders make the self-care problems classification process complex and time-consuming. To overcome this challenge, an expert model is proposed innovatively in this research. The proposed model is based on Probabilistic Neural Network (PNN) and Genetic Algorithm (GA) for classifying self-care problems of children with physical and motor disability. In this model, PNN is employed as a classifier and GA is applied for feature selection. The PNN is trained by using a standard ICF-CY dataset. Based on ICF-CY, occupational therapists must evaluate many features to diagnose self-care problems. According to the experiences of occupational therapists, these features have different effects on classification. Hence, GA is employed to select relevant and important features in self-care problems classification. Since the classification rules are important for occupational therapists, the self-care problems classification rules are extracted additionally by using the CART algorithm. The experimental results show that by using the feature selection algorithm, the accuracy and time complexity of classification are improved in comparison to other models. The proposed model can classify self-care problems of children with 94.28% accuracy by using only 16.5% of all features.     

基于神经网络的变压器故障诊断方法研究

针对概率神经网络(PNN)模型强大的非线性分类能力,PNN能够很好地对变压器故障进行分类;文章通过对PNN神经网络的结构和原理的分析,应用PNN概率神经网络方法对变压器故障进行诊断;通过实例仿真表明,PNN网络的训练时间比BP网络少,比之预测准确度也要高,而且还具有高度的泛化能力,这使得PNN网络可以有效地运用到变压器故障诊断中,具有一定的可操作性。     

An enhanced classification approach for reliability estimation of structural systems

The accuracy of a classification-based surrogate model for reliability assessment can be improved by augmenting the training data (labeled data or data with known responses) with a large number of unlabeled data (data with unknown responses) in semi-supervised learning methods. In this research, an enhanced Probabilistic Neural Network (PNN) algorithm is proposed where the Gaussians at each labeled point are not assumed to be spherical. Each of the Gaussians has a ‘full’ covariance matrix instead of simply assuming the Gaussian with a ‘spherical’ covariance matrix. First, the Expectation-Maximization algorithm is applied on the labeled and unlabeled data while assuming that the number of ‘full’ Gaussians is equal to the number of labeled datapoints. The contribution of each of these ‘full’ Gaussians at a particular datapoint is found by using the Bayes Theorem. The Bayes decision criterion is then used in the final output layer of the PNN to classify test patterns into either the safe or the failure class. The primary benefit of the proposed method comes from utilizing unlabeled data for better estimation of ‘full’ covariance matrices of constituting Gaussian clusters of underlying data, which are then used to estimate the Probability Density Functions of classes for classification. This procedure does not require additional computational costs to improve the accuracy of the classification results since the cost of unlabeled data is negligible in general. Two examples including an analytic problem and a truss problem are presented in order to validate the proposed reliability estimation process. The results reflect considerable improvements of the classifier performance for estimating reliability while maintaining sufficient accuracy.