汽车发动机故障神经网络模式识别及试验研究

介绍了汽车发动机故障模式识别对汽车工业的意义,并用BP神经网络对汽车发动机机械故障进行分类,实验证明这种方法有很高的识别率．     

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。     

BP网络学习算法研究及其图像模式识别应用

BP神经网络的训练性能主要包括收敛速度和收敛精度等方面,该训练性能在很大程度上取决于所选择的网络学习算法。本文针对模式识别应用领域,通过实例,运用Matlab编程对部分较好的网络学习算法的训练性能进行了比较研究．给出了模式识别应用过程中BP网络学习算法的选择依据。在此基础上,运用BP网络编程实现了对常见单位符号图像目标的模式识别,并通过识别算法的实现过程,分析了用BP网络进行图像目标模式识别的具体思路和方法。     

基于神经网络的平移旋转不变模式识别

本文将经二进制编码的不变性矩作为神经网络的输入进行不变性模式识别,计算机模拟结果表明,神经网络与不变性矩的结合不仅对输入模式具有位移旋转不变的性能,而且有较好的容错性。     

多层BP网络的研究及应用

在本文中，我们用统计模式识别的方法分析了目前在模式识别中得到广泛应用的多层ＢＰ神经网络，揭示了具有线性输出单元的多层ＢＰ神经网络用作特征提取器和分类器时具有良好性能的原因。同时，我们设计了一个使用ＢＰ网络作为特征提取器和分类器时，普适的工件识别系统，对不能识别的样本，采用模糊推理技术，把传统的直观特征识别结果和多层ＢＰ网络结果特征级上融合，提高系统的性能。     

基于Mel倒谱的某型飞机发动机振动故障的模式识别

根据人耳听觉特性,利用Mel倒谱系数提取有利于发动机振动故障识别的特征;前向神经网络的反向传播算法对类别数目小、但分类困难的模式识别问题有良好的分类效果;针对Mel倒谱系数分析提取的发动机振动故障分类的特征,分别采用BP网络和RBF网络对飞机发动机振动故障进行识别;结果表明:Mel倒谱系数的维数可以改变,选择合适的的Mel特征参数可以有效地提高发动机故障的识别率;RBF网络的故障识别率较高。     

基于CS和神经网络的传感器网络模式识别研究

当前,传感器网络快速地发展,其节点不断增多,需要传输和储存的数据量也就不断增大,同时现有传感器节点尺寸与复杂度限制了传感器网络的数据存储、数据的计算速度及频宽.针对此类问题,将压缩感知技术应用于传感器网络,对传感器网络中传输的数据进行压缩,降低传输数据量,然后在接收端重构数据,将该数据样本作为BP网络的输入进行识别.实...     

基于BP网络的字符模式识别

神经网络理论已经成为解决某些问题的重要手段的方法.但利用神经网络进行解决问题和设计的时候,必定会涉及到大量的有关数值计算等问题,所以利用计算机对神经网络模型进行仿真和辅助设计时,仍是件很麻烦的事情.所以MATLAB的便利受到了青睐,BP网络在人工神经网络中应用最为广泛,而且在理论上十分完善,网络结构也比较直观.在BP网络中,模式识别是应用比较广泛的一个方面.该系统使用MATLAB中神经网络工具箱对英文表中的26个字母进行识别.通过建立网络,训练网络,测试网络,最后进行仿真,完成了正确识别26个英文字母的功能.该系统的操作界面简洁、友好、使用简单方便.     

Some methods for pattern recognition

The present study is dedicated to pattern recognition of alphabetical and numerical symbols. A recognition method based on modification of Hausdorff metrics is presented; then, another method named the method of radial neighborhoods is described. A series of experiments on test patterns are conducted.     

模式识别方法在入侵检测中的应用

将模式识别方法应用到入侵检测领域,用以区分正常和异常的用户或主机行为。采用KDD99作为实验数据集,通过计算信息增益,从原始数据中选取对分类结果影响较大的特征属性;再分别选取两种带监督的模式识别方法支持向量机(SVM)和多层神经网络(MNN)以及两种不带监督的聚类方法Single-Linkage和K-Means进行实验。实验结果表明,上述方法在入侵检测领域中具有很好的应用前景。     

Growing subspace pattern recognition methods and theirneural-network models

In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning subspace methods (LSMs) have the potential to integrate feature extraction and classification. In this paper, we propose two new algorithms, along with their neural-network implementations, to overcome certain limitations of the earlier LSMs. By introducing one cluster at a time and adapting it if necessary, we eliminate one limitation of deciding how many clusters to have in each class by trial-and-error. By using the principal component analysis neural networks along with this strategy, we propose neural-network models which are better in overcoming another limitation, scalability. Our results indicate that the proposed classifiers are comparable to classifiers like the multilayer perceptrons and the nearest-neighbor classifier in terms of classification accuracy. In terms of classification speed and scalability in design, they appear to be better for large-dimensional problems.     

灵芝指纹图谱的建立及模式识别

以22个不同产地不同品种灵芝样本的红外光谱图为基础,建立了灵芝FTIR指纹图谱,共分为6个区,并标示出了指纹区,各灵芝样本图谱(除灵芝孢子粉)和灵芝FTIR指纹图谱的相似度均在90%以上.通过三甲基硅烷(TMS)衍生化反应,对各灵芝样品进行GC-MS测定,建立了灵芝TMS衍生物指纹图谱,共标示出19个特征峰(糖区含12个特征峰).应用化学计量学方法中的PCA投影法对GC-MS积分数据作PCA投影可以将22个灵芝样本分为海南产地和北方产地两大类.应用欧式距离聚类分析法对FTIR光谱数据及GC-MS积分数据进行欧式距离计算,22个灵芝样本分别被聚为10个和9个分类区,均达到了聚类的效果,同时又均表现出共属"灵芝"一类的本质.     

Automatic classification of grains via pattern recognition techniques

During production, storage and shipment, a crop of feed grain is often contaminated with admixtures of other grains or foreign material. Failure to meet the contractural limits of purity can result in monetary penalties or may prevent completion of the sales transaction. Presently, purity is established by human inspectors. They subject a carefully selected sample to 100 per cent visual inspection.This paper describes part of the development of a high speed, automatic sorting and grading procedure. A recursive learning pattern classification scheme is described which yields an overall accuracy of about 98 per cent.     

Synthesis of quasi-invariant controllers using pattern recognition methods

The study presents a new approach to the synthesis of multidimensional quasi-invariant control systems based on the formulation and solution of the synthesis problem as an pattern recognition problem. The capabilities and specific features of the new approach are illustrated by two mathematical models of control systems.     

Secure identification documents via pattern recognition andpublic-key cryptography

We present an approach to authenticating photo-ID documents that relies on pattern recognition and public-key cryptography and has security advantages over physical mechanisms that currently safeguard cards, such as optical laminates and holograms. The pattern-recognition component of this approach is based on a photo signature that is a concise representation of the photo image on the document. This photo signature is stored in a database for remote authentication or in encrypted form on the card for stand-alone authentication. Verification of the ID document entails scanning both the photo image and a machine-readable form of the text information, determining the photo signature, and comparing this information against the reference. In this paper, we describe the method and present results of testing a large database of images for photo-signature match in the presence of noise     

Rough sets in hybrid methods for pattern recognition

The article shows how rough sets can be applied to improve the classification ability of a hybrid pattern recognition system. The system presented here consists of a feature extractor based on a computer‐generated hologram (CGH) playing the role of a ring‐wedge detector. Features extracted by it are shift, rotation, and scale invariant. Although they can be optimized, no method has been proposed in the literature. This article presents an original method of optimizing the feature extraction abilities of a CGH. The method uses rough set theory (RST) to measure the amount of essential information contained in the feature vector. This measure is used to define an objective function in the optimization process. Since RST‐based factors are not differentiable, we use a nongradient approach for a search in the space of possible solutions. Finally, RST is used to determine decision rules for the classification of feature vectors. The alternative method of classification based on neural networks is also discussed. The whole method is illustrated by a system recognizing the class of speckle pattern images indicating the class of distortion of optical fibers. © 2001 John Wiley & Sons, Inc.     

Rough sets in hybrid methods for pattern recognition

Many papers describe hybrid methods used for pattern recognition. Such systems consist of an optical part, performing fast signal preprocessing, and a computer, analyzing preprocessed data. Here we present the method which uses for feature extraction the ring‐wedge detectors (RWD) or computer generated holograms (CGH) serving as RWD. Features obtained in this way are shift, rotation, and scale invariant, but papers suggest that they can be still subject for further optimization. This article presents an original method for optimizing feature extraction abilities of CGH. This method uses rough set theory (RST) to measure the amount of essential information for the classification, preserved in feature vector. As there is no gradient direction information in factors defined by RST, we use for a space search a stochastic evolutionary approach. Finally, we use RST to determine decision rules for the feature vector classification. The whole method is illustrated by a system recognizing the speckle pattern images obtained as a result of interference of light going through a quasi‐monomode optical fiber. As the conditions of interference differ when some kind of distortion of the optical fiber is produced, such a system can be used as a sensor of the pressure causing this distortion. © 2000 John Wiley & Sons, Inc.