网状聚类算法在横河DCS故障分析系统中的应用

通过最小化目标函数分析研究,提出了网状聚类算法,阐述并实现了簇心全连通和簇心连通模型,同时以CENTUMCS3000 R3和V B的OCX控件为平台,开发了某化工厂爆炸监控系统。实践表明,聚类结果与监控设备运行结果吻合得很好,满足了化工厂监控系统的实际需求,证明该聚类算法和应用模型具有较高的理论和实用价值。     

基于自组织竞争神经网络的智能交通信号灯控制

为了解决拥挤的城市交通问题,针对交通灯监控系统中可变的交通状况,提出一种基于自组织神经网络算法的监控系统。应用优化模型参实现交通灯的控制,使道路通畅。对监控系统进行分析,合理选择优化的模型参数,根据动态的车流量,通过模式分类有效分配当前车道的通行时间,并全面考虑同时通行的各种车道组合。该监控系统提高了车辆通行效率,使道路更为通畅。与传统的固定配时系统相比,它更适于当前纷繁复杂的交通状况。     

模糊聚类分析在乳腺癌图像分类中的应用

研究模糊聚类分析在医学图像数据挖掘中的应用。利用决策树算法对乳腺癌图像数据进行分类，实现了一个基于决策树算法的医学图像分类器，获得了分类的实验结果。该模型系统达到了较高的分类准确率，证明数据挖掘在辅助医疗诊断中有着广泛的应用前景。     

基于Milter实现的中文垃圾邮件过滤系统

提出一种基于内容的中文垃圾邮件实时过滤系统的实现方案，该系统建立在Linux的Sendmail邮件服务器上，通过Milter接口实时提取邮件内容，并结合中文分词及文本分类算法对邮件实施分类和过滤。该系统可嵌入多种文本分类算法，具有良好的可扩展性。通过测试对该系统内嵌入的不同分类算法模型进行了分析和比较。     

SVM算法在网站分类中的应用研究

研究SVM算法在中文网站顶级类别分类中的应用,并提出一个网站向量模型的生成算法.该算法以网站的首页HTML代码为基础,通过代码清理、内容提取、分词等步骤生成向量模型作为SVM的输入,并采用多类的线性SVM分类器进行分类.最后对这个算法进行了实现,通过实际的网站数据测试,分析了实验结果,证实了这个算法能够取得较高的分类准确率并表明了在低流量低延时状态下网站分类系统的可行性,为其进一步在客户端或游览器中的应用提供了实验基础.     

改进蚁群算法MMAS在分类规则挖掘中的研究

为深入研究和评估蚁群算法在分类规则挖掘应用中具有的特点和作用,针对目前基本蚁群算法在数据挖掘方面所存在的不足,引入了改进的蚁群算法模型最大最小蚂蚁系统（MMAS）。并根据分类算法比较原则,通过实验分析对分类规则挖掘算法进行比较。根据使用不同数据集实验结果的对比分析,从仿真的精确度、速度等方面展示和证实了基于改进的蚁群算法模型MMAS的数据分类规则挖掘工具AntMiner＋在分类规则挖掘中体现出的特点和优势。     

数据挖掘分类算法在入侵检测应用中的比较

针对当前入侵检测系统普遍存在的误报、漏报和缺乏自适应性问题,采用ODM的分类算法中的决策树分类算法、支持向量机分类算法、朴素贝叶斯算法和二元变量逻辑回归算法等四种重点技术对实验数据进行模型建立和测试．并通过对四种应用结果准确度的对比分析证明本文所采用的技术路线的可行性和生成结果的准确性．从中找出在实际应用中较为常用、直接、有效的和有一定通用价值的方法。     

基于霍夫曼树SVM的监控视频清晰度评价

为提高视频监控系统中视频清晰度评价的准确性和系统运行效率,提出了结合提升小波变换和支持向量机(SVM)分类算法的图像清晰度评价算法.在研究了常用的SVM多类分类算法的基础上,提出了霍夫曼树SVM多类分类算法.进行了视频监控系统的仿真实验,结果表明,与普通二叉树SVM多类分类算法的效果相比,霍夫曼树SVM多类分类算法在系统的分类正确率及运行效率方面有明显提升.     

基于多种聚类算法和多元线性回归的多分类主动学习算法

针对传统岩性识别方法识别精度低，难以和地质经验有机结合的问题，提出了一种基于多种聚类算法和多元线性回归的多分类主动学习算法（ALCL）。首先，通过多种异构聚类算法聚类得到对应每种算法的类别矩阵，并通过查询公共点对类别矩阵进行标记和预分类；其次，提出优先级最大搜寻策略和最混乱查询策略选取用于训练聚类算法权重系数模型的关键实例；然后，定义目标求解函数，通过训练关键实例求解得到每种聚类算法的权重系数；最后，结合权重系数进行分类计算，从而对结果置信度高的样本进行分类。应用大庆油田油井的6个公开岩性数据集进行实验，实验结果表明，ALCL的分类精度最高时，比传统监督学习算法和其他主动学习算法提高了2.07%~14.01%。假设检验和显著性分析的结果验证了ALCL在岩性识别问题上具有更好的分类效果。     

车辆监控调度系统中兴趣模型设计与研究

针对车辆监控调度系统的各种应用需求,抽象三类GPS监控兴趣模型：位置兴趣模型、路径兴趣模型和区域兴趣模型;充分利用车辆定位的历史轨迹信息,构建以上各类模型的解决方案和具体算法。实验结果证明算法具有很好的有效性和可靠性。     

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses.     

On the construction and training of reformulated radial basis function neural networks

Presents a systematic approach for constructing reformulated radial basis function (RBF) neural networks, which was developed to facilitate their training by supervised learning algorithms based on gradient descent. This approach reduces the construction of radial basis function models to the selection of admissible generator functions. The selection of generator functions relies on the concept of the blind spot, which is introduced in the paper. The paper also introduces a new family of reformulated radial basis function neural networks, which are referred to as cosine radial basis functions. Cosine radial basis functions are constructed by linear generator functions of a special form and their use as similarity measures in radial basis function models is justified by their geometric interpretation. A set of experiments on a variety of datasets indicate that cosine radial basis functions outperform considerably conventional radial basis function neural networks with Gaussian radial basis functions. Cosine radial basis functions are also strong competitors to existing reformulated radial basis function models trained by gradient descent and feedforward neural networks with sigmoid hidden units.     

基于径向基函数神经网络的红外步态识别

为提高红外步态识别的效果,提出一种基于径向基函数神经网络的多分类器融合算法。对红外步态序列,分别应用基于轮廓线傅立叶描述子特征的模糊分类器和基于下肢关节角度特征的贝叶斯分类器进行识别,再利用径向基函数神经网络的学习和分类功能,对获得的输出信息进行度量层的融合和再识别。仿真实验结果表明,该算法获得更加精确的分类效果。     

径向基概率神经网络的一种自组织学习算法

介绍了径向基概率神经网络 (RBPNN)的一种自组织学习算法 ,该算法把径向基概率神经网络的结构原理与自组织聚类算法相结合 ,不仅能够完成对训练样本的聚类分析 ,标识出训练样本的类别属性 ,而且能够自动完成基于该训练样本集的径向基概率神经网络的训练过程 .本算法用于对 IRIS三种花型识别在训练阶段达到 97.33%的识别效果 ,而在推广能力方面 ,由本文算法得到的 RBPNN优于有标识的训练样本的 RBFNN     

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

组合核函数SVM在特定领域文本分类中的应用

面向特定领域文本分类的实际应用,存在大量样本相互掺杂的现象,使其无法线性表述,在SVM中引入核函数可以有效地解决非线性分类的问题,而选择不同的核函数可以构造不同的SVM,其识别性能也不同,因此,选择合适的核函数及其参数优化成为SVM的关键.本文基于单核核函数的性质,对多项式核函数与径向基核函数进行线性加权,构建具有良好的泛化能力与良好的学习能力的组合核函数.仿真实验结果表明,在选择正确参数的情况下,组合核函数SVM的宏平均准确率、宏平均召回率及宏平均综合分类率都明显优于线性核、多项式核与径向基核,而且能够兼顾准确率与召回率.     

基于核聚类进化算法的音乐情感模糊计算模型

音乐情感计算涉及到多维度多层次结构的复杂情感表征问题,而情感本身所具有的模糊性、细微性和多样性,使得传统的情感识别方法普遍效率低下且正确率不高.为提高识别精度,首先利用高斯径向基函数进行非线性映射,来分辨、提取并放大更多的细节信息.然后通过深入剖析中国古琴乐曲,从中抽取出影响最大的六个情感特征值,并在非线性映射的基础上,构造一种基于核聚类进化算法的音乐情感模糊计算模型.在此基础上,进一步针对算法中统一设定簇半径阈值的不足,提出基于蚁群算法的规则调整策略,并进行系统实验.实验结果表明,与基于概率统计的Beyes分类方法相比,优化后的模糊计算模型具有较好的识别效果.     

基于支持向量机的睡眠结构分期研究

为了提高睡眠结构分期的准确度,克服分类时样本不足对分类的影响,使用MIT-BIH数据库整晚睡眠脑电数据作为研究样本,提取了时域、频域和非线性共16个参数作为分类特征,用支持向量机的一对一多类分类方法,采用顺序最小优化算法,以径向基函数作为核函数对样本分类。分类结果与专家的分类标注对比,分类准确率达到92%以上。支持向量机可作为睡眠分期的一种实用算法。