结合概率型神经网络(PNN)和学习矢量量化(LVQ)算法的文本分类方法

针对文本自动分类问题,提出一种基于概率型神经网络(PNN)和学习矢量量化(LVQ)相结合的文本分类算法,该方法借助TFIDF方法提取文本特征及特征值,形成文本分类特征向量,利用概率型神经网络构建分类模型,并利用LVQ学习算法对神经网络模型竞争层网络进行学习,使相应模式向量相互靠拢,远离其他模式,从而实现文本分类.实验结果表明,提出的该方法在文本分类中表现了很好的效果,不仅具有很好的分类准确率,还表现出很好的学习效率.     

Power quality disturbance detection and classification using wavelet and RBFNN

This paper introduces a novel approach to detect and classify power quality disturbance in the power system using radial basis function neural network (RBFNN). The proposed method requires less number of features as compared to conventional approach for the identification. The feature extracted through the wavelet is trained by a radial basis function neural network for the classification of events. After training the neural network, the weight obtained is used to classify the Power Quality (PQ) problems. For the classification, 20 types of disturbances are taken into account. The classification performance of RBFNN is compared with feed forward multilayer network (FFML), learning vector quantization (LVQ), probabilistic neural network (PNN) and generalized regressive neural network (GRNN). The classification accuracy of the RBFNN network is improved, just by rewriting the weights and updating the weights with the help of cognitive as well as the social behavior of particles along with fitness value. The simulation results possess significant improvement over existing methods in signal detection and classification.     

LVQ神经网络在磁瓦表面缺陷分类中的运用

在基于磁瓦表面缺陷图像直方图、纹理、投影和形状的特征提取的基础上,提出了一种用LVQ神经网络进行缺陷分类的方法,对现场采集到的6种主要缺陷类型进行了试验。试验结果表明,基于LVQ神经网络的分类器训练与分类的时间短,多缺陷种类分类时准确率高。     

Detection of Seizure Activity in EEG by an Artificial Neural Network: A Preliminary Study

Neural networks, inspired by the organizational principles of the human brain, have recently been used in various fields of application such as pattern recognition, identification, classification, speech, vision, signal processing, and control systems. In this study, a two-layered neural network has been trained for the recognition of temporal patterns of the electroencephalogram (EEG). This network is called a Learning Vector Quantization (LVQ) neural network since it learns the characteristics of the signal presented to it as a vector. The first layer is a competitive layer which learns to classify the input vectors. The second, linear, layer transforms the output of the competitive layer to target classes defined by the user. We have tested and evaluated the LVQ network. The network successfully detects epileptiform discharges (EDs) when trained using EEG records scored by a neurologist. Epochs of EEG containing EDs from one subject have been used for training the network, and EEGs of other subjects have been used for testing the network. The results demonstrate that the LVQ detector can generalize the learning to previously “unseen” records of subjects. This study shows that the LVQ network offers a practical solution for ED detection which is easily adjusted to an individual neurologist's style and is as sensitive and specific as an expert visual analysis.     

基于LVQ神经网络的改进覆盖算法

覆盖算法是一种具有高分类准确度和强泛化能力的构造性神经网络分类算法。针对其选择覆盖中心的随意性,结合竞争性神经网络方法对覆盖算法进行改进,在覆盖学习之前进行预学习,选择最佳覆盖球形中心,来优化覆盖。通过标准UCI测试数据实验的比较,从分类的准确性和覆盖个数方面进行对比,得到改进的覆盖算法有很好的效果。     

基于LVQ网络的航空发动机气路故障特征提取方法研究

针对航空发动机预测与健康管理系统对其状态判断和故障诊断的需求,结合LVQ网络具有处理分类问题时能够识别信息内含有的重要聚类特征信息的优点,提出了基于LVQ神经网络的航空发动机故障特征提取方法。分析研究了LVQ神经网络的结构和学习算法,以及某型航空发动机的测量参数、数据预处理和故障样本选取方法。并以其设计点为例进行了系统仿真。通过与BP网络的分类器对比试验,表明了该算法的可行性和有效性。     

A Compact Convolutional Neural Network Augmented with Multiscale Feature Extraction of Acquired Monitoring Data for Mechanical Intelligent Fault Diagnosis

Considering all the monitoring data of bearings until failure, very few data are acquired when the bearings are faulty. Such circumstance leads to small faulty sample problem when an intelligent fault diagnosis method is applied. A deep neural network trained with small samples cannot be trained completely, and tends to overfit, which results in poor performance in practical application. To solve this problem, a compact convolutional neural network augmented with multiscale feature extraction is proposed in this paper. Multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated. Besides, a specially designed compact convolutional neural network synthetically analyzes the multiscale features. By combing these two tricks, the proposed neural network can extract more sensitive features with a relatively shallow structure, which increases classification accuracy under small samples. Dropout technique is also used to prevent the network from overfitting. Effectiveness of the proposed method is verified by three bearing datasets. Experiments show that this network can achieve competitive results with limited training samples even with different load and mixed rotating speed.     

Extraction rice-planted areas by RADARSAT data using neural networks

A classification technique using the neural networks has recently been developed. We apply a neural network of learning vector quantization (LVQ) to classify remote-sensing data, including microwave and optical sensors, for the estimation of a rice-planted area. The method has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data were observed before and after planting rice in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. Three RADARSAT images from April to June were used for this study. The LVQ classification was applied the RADARSAT and SPOT to evaluate the estimate of the area of planted-rice. The results show that the true production rate of the rice-planted area estimation of RADASAT by LVQ was approximately 60% compared with that of SPOT by LVQ. It is shown that the present method is much better than the SAR image classification by the maximum likelihood method.     

基于样本熵和模式识别的脑电信号识别算法研究

脑-机接口BCI是一种实现人脑和外部设备通信的新兴技术。基于时频特性进行特征提取的传统方法无法体现EEG信号的非线性特征。为了进一步提高分类的准确率,首先采用小波阈值降噪的预处理方法提高了EEG信号的信噪比。然后结合非线性动力学的样本熵参数,对3种想象运动的脑电信号进行特征提取,保留了脑电信号的非线性特征。其中,运动想象MI脑电信号的研究一直都是BCI这一高速发展领域的重点目标。还研究了支持向量机、LVQ神经网络和BP神经网络3种分类器。通过实验结果对比发现,BP神经网络具有较高的识别率,更适用于脑电信号的分类识别。     

基于双池化特征加权结构CNN的图像分类

传统的池化方式会造成特征信息丢失,导致卷积神经网络中提取的特征信息不足。为了提高卷积神经网络在图像分类过程中的准确率,优化其学习性能,本文在传统池化方式的基础上提出一种双池化特征加权结构的池化算法,利用最大池化和平均池化2种方式保留更多的有价值的特征信息,并通过遗传算法对模型进行优化。通过训练不同池化方式的卷积神经网络,研究卷积神经网络在不同数据集上的分类准确率和收敛速度。实验在遥感图像数据集NWPU-RESISC45和彩色图像数据集Cifar-10上对采用几种池化方式的卷积神经网络分类结果进行对比验证,结果分析表明：双池化特征加权结构使得卷积神经网络的分类准确率有很大程度的提高,同时模型的收敛速度得到进一步提高。     

基于差异演化概率神经网络的纹理图像识别

引入差异演化（DE）算法来弥补基本概率神经网络的不足,从而提出一种基于差异演化概率神经网络的纹理图像识别方法。首先用树形结构小波包变换提取纹理图像的能量特征,用基于统计的纹理特征方法提取统计均值、平均能量、标准差和平均残余特征,得到纹理图像的特征矢量;然后用差异演化概率神经网络训练纹理图像的特征矢量,从而实现纹理图像的识别。实验结果表明：该方法较BP神经网络、RBF神经网络和基本的PNN有更高的识别正确率,且收敛更快。     

Rotating machine fault detection based on HOS and artificial neural networks

In order to identify the faults of rotating machinery, classification process can be divided into two stages: one is the signal preprocessing and the feature extraction; the other is the recognition process. In the preprocessing and feature extraction stage, the higher-order statistics (HOS) is used to extract features from the vibration signals. In the recognition process, two kinds of neural network classifier are used to evaluate the classification results. These two classifiers are self-organizing feature mapping (SOM) network for collecting data at the initial stage and learning vector quantization (LVQ) network at the identification stage. The experimental results obtained from HOS as preprocessor to extract the features of fault are clearer than those obtained from the power spectrum. In addition, the recognizable rate by using either SOM or LVQ as classifiers is 100%.     

基于改进概率神经网络的纹理图像识别

引入差异演化（DE）算法来弥补基本概率神经网络的不足,从而提出一种基于改进概率神经网络（MPNN）的纹理图像识别方法。首先用树形结构小波包变换提取纹理图像的能量特征,用基于统计的纹理特征方法提取统计均值、平均能量、标准差和平均残余特征,得到纹理图像的特征矢量;然后用改进的概率神经网络训练纹理图像的特征矢量,从而实现纹理图像的识别。实验结果表明：采用基于改进概率神经网络的纹理图像识别方法较BP神经网络、RBF神经网络和基本的PNN有更高的识别正确率,且收敛更快。     

基于卷积神经网络的通信信号调制识别研究

针对传统人工提取专家特征来进行通信信号识别的方法存在局限性大、低信噪比下准确率低的问题,提出一种复基带信号与卷积神经网络自动调制识别相结合的新方法。该方法将接收到的信号进行预处理,得到包含同相分量和正交分量的复基带信号,该信号作为输入卷积神经网络模型的数据集,通过多次训练调整模型结构以及卷积核、步长、特征图和激活函数等超参数,利用训练好的模型对通信信号进行特征提取和识别。实现了对2FSK、4FSK、BPSK、8PSK、QPSK、QAM16和QAM64 七种数字通信信号类型的识别分类。实验结果表明,当信噪比为0dB时,七种信号的平均识别准确率已达94.61%,验证了算法是有效的且在低信噪比条件下有较高的准确率。     

A new genetic feature selection with neural network ensemble

A neural network ensemble is a learning paradigm in which a finite collection of neural networks is trained for the same task. Ensembles generally show better classification and generalization performance than a single neural network does. In this paper, a new feature selection method for a neural network ensemble is proposed for pattern classification. The proposed method selects an adequate feature subset for each constituent neural network of the ensemble using a genetic algorithm. Unlike the conventional feature selection method, each neural network is only allowed to have some (not all) of the considered features. The proposed method can therefore be applied to huge-scale feature classification problems. Experiments are performed with four databases to illustrate the performance of the proposed method.     

基于特征整合的卷积神经网络草地分类算法

为提高遥感影像草地分类的精度,分析了卷积神经网络中提取图像特征的特点,提出了一种基于特征整合深度神经网络的遥感影像特征提取算法。首先,将遥感影像数据进行PCA白化处理,降低数据之间的相关性,加快神经网络学习的速率;其次,将从卷积神经网络中提取到的浅层特征和深层特征进行双线性整合,使得整合后的新特征更加完善和优化;最后,对遥感数据进行训练,由于新特征中有效信息的增加,使得特征表达能力得到提高,达到提高草地分类准确率的目的。实验结果表明:该算法能够有效地提高草地分类的准确率,分类精度达到94.65%,相较于卷积神经网络、BP神经网络和基于SVM的分类算法分别提高了4.3%、10.39%和15.33%。     

Discriminative training approaches to fabric defect classification based on wavelet transform

Wavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved.     

基于K-means聚类和LVQ神经网络的OLED缺陷像素识别

提出改进的K-means聚类分割和LVQ神经网络分类的方法，用于有机发光二极管显示面板喷墨打印制程中缺陷像素的识别。首先采用改进的K-means聚类算法对预处理后的打印像素进行分割，然后采用连通域水平矩形确定每一个打印像素的坐标及几何特征，再通过灰度共生矩阵提取其纹理特征，最后通过LVQ神经网络对所述特征进行分类，完成缺陷像素的标记及分类统计。结果表明，本文算法的识别率明显优于其他常用分类识别算法，平均缺陷检测率为100%，分类准确率达到98.9%，单像素检测时间为8.3 ms。