基于双谱分布区域的齿轮聚类分析与故障诊断

齿轮(尤其是故障齿轮)振动信号具有明显的非高斯性,双谱是分析非高斯信号的有效方法.齿轮不同故障模式振动信号双谱分析表明齿轮双谱分布区域与故障模式间存在映射关系,可作为分类特征.采用阈值化的双谱二值图作为特征向量,基于目标函数的聚类法实现分类与故障诊断.聚类过程以训练样本双谱二值图的逻辑与运算表征类间分布共性,逻辑或运算表征类间分布范围,构成类模板,测试样本与类模板距离最小值构造最近邻模板分类器,实现聚类,整个过程只是计算1的个数,简单、实用.齿轮故障诊断实例验证了该方法的有效性.     

基于目标域自适应SVM分类器的微博情绪分类

目前微博数据上难以实现跨领域情绪分类,很多基于特征和实例的方法表现出分类准确度低和计算速度慢等问题.针对此问题,本文提出一种基于模型的自适应支持向量机(SVM)的微博情绪分类方法,简称MASVM.该方法可以将现有模型直接应用到新目标域数据.首先,在"多对一"SVM自适应模型的基础上,对源域数据集训练的分类器决策函数进行修改,创建出一个适应目标域分类器.然后,扩展多个分类器适应框架,根据基础分类器在较小目标域标签样本集合中的分配性能,学习基础分类器的权重控制.最后,最大限度使用通用语料库训练出的基础分类器,通过领域适应,将情绪分类的分类误差最小化.实验结果表明,所提方法在准确度和计算效率方面优于基准方法和其他类似方法的性能.另外,目标域标签数据的不同比例也会对分类性能造成影响.由于所提方法的性能优于域外基础模型和域自适应方法,表现出了与域内上界模型相近的性能,且具有计算速度优势,可以作为一种快速微博情绪分类方法加以应用。     

异构分类器融合环境下的非平衡数据分类模型

为解决非平衡数据分类中的正样本分类精度不高的瓶颈问题,提出了一种异构分类器融合环境下的非平衡数据分类模型.该模型基于差异采样率的重采样算法和改进的Adaboost算法,融合了SVM和C5.0两种基分类器;基于知识融合机制,采用了独特的分类器选择策略、分类器集成方法、分类决策方案.仿真实验结果表明,SCECM模型分类性能...     

基于支持向量机的步态分类方法

针对小样本步态数据引起的分类器泛化能力差的问题,提出了基于支持向量机的步态分类方法.采集了24名青年和24名老年受试者的步态数据,提取24个步态特征训练支持向量机,采用交叉验证方法评估分类器的泛化性能.结果表明,本文提出的方法能够有效地对小样本步态数据分类,并且具有良好的泛化性.不同的核函数对分类性能影响较小.与传统反向传播学习算法的神经网络分类器进行了比较,支持向量机分类性能明显优于传统反向传播学习算法的神经网络.支持向量机在步态分类中具有广泛的应用前景.     

用于手写汉字识别的改型Adaboost算法

针对大部分多类Adaboost算法因训练复杂度过高而难以应用于手写汉字识别这种大类别数分类的问题,提出了一种新的改型的多类Adaboost算法.该算法采用基于描述性模型的多类分类器--改进的二次鉴别函数(MQDF)分类器作为基元分类器,可直接进行多类分类,无需将多类问题转化为多个两类问题处理,大大降低了训练复杂度.此外,该算法根据广义置信度更新样本权重,实验证明此方法简单有效.为了降低算法的识别复杂度,对训练后得到的基元分类器组进行删减,仅保留一个最优的基元分类器作为最终分类器.在HCL2000及THOCR-HCD数据集上进行的实验表明,该算法的相对错误率比现有算法分别下降了14.3%、8.1%和19.5%.     

一种粗模糊神经分类器

介绍一种新的粗集编码模糊神经分类器。基于粗集理论的概念，讨论了知识编码、属性简化、分类系统简化的方法；并利用模糊隶属度函数将输入精确信息映射为模糊变量信息，解决分类中病态定义的数据问题和提高系统非线性映射的分类能力；提出了结合系统参数的重要性因子的网络的模糊推理方法和粗模糊神经分类器的网络结构以及有导师的最小平方误差学习训练算法。实现的粗集编码模糊神经分类器具有网络结构空间维数低、学习算法简单、网络训练时间短、非线性特性丰富等优点。     

基于共形几何代数与二次规划的分类器设计

提出一种基于共形几何代数与二次规划的分类器设计方法.从新的角度出发,讨论了运用共形几何代数理论来构造最优分类超球可分问题的可行性和简便性,首先介绍了基于共形几何代数的分类超球面的几何表示,并用此表示将二类最优分类超球面的可分问题转化二次规划的训练学习问题,在此基础上分析了多类分类器的设计和训练方法.该算法保留了最大分类间隔理论的优点,将二类最优平面可分推广到最优超球可分,简化了其运算复杂度,仿真实验表明,该学习算法简洁明确,对于算法的集成,提高效率有着很重要的意义.     

基于PCNN的海冰SAR图像分类系统

简化和改进了脉冲耦合神经网络（PCNN）,建立了基于时间索引图的脉冲耦合神经网络海冰SAR图像分类器,用于海冰SAR图像的分割和海冰分类。在此基础上建立了基于人工解译的半自动海冰分类判读系统。将发展的分类器用于辽东海湾冰探测,结果表明这个分类器能够区分海冰和海水,识别不同海冰类型,且具有高效率。为了选择适合辽东湾海冰分类的PCNN参数,分析了链接半径、链接强度和索引图等级等参数,给出了各参数合适的取值范围及调节原则。     

基于模糊K-均值算法的模糊分类器设计

基于模糊K-均值算法的模糊分类器,就是把目前比较常用的模糊K-均值算法的聚类方法,再一次与模糊分类规则提取相结合而得到的一种分类器。它是一种很有效的模糊分类器,训练样本能正确的分类。在这种方法中,首先用模糊K-均值算法按剖分和覆盖的原则把训练样本分成群,并且每一群的中心和半径都被计算出来。然后,设计一个用模糊规则来表示分类的模糊系统。这样就有效地构建了一个能对训练样本比较准确分类的模糊分类器。用这种方法设计的分类器不需要预定义参数、训练时间较短、方法简单     

基于LZ复杂性距离的蛋白质三维结构比较

基于LZ复杂性距离提出了一种非比对的蛋白质三维结构比较方法.该方法以蛋白质结构单元间的条件LZ复杂性为特征参数,根据条件LZ复杂性计算LZ复杂性距离来作为蛋白质三维结构(不)相似程度的定量刻画.该方法可在二次多项式的时间限度内计算完成.蛋白质的结构数据采用接触图的表示方式,以避免PDB格式数据中的非结构信息和不同坐标系对结构比较的影响.以真实的蛋白质三维结构数据所组成的5个数据集为实例,基于LZ复杂性距离对各数据集中的蛋白质单链进行了结构聚类.聚类的结果符合各蛋白质单链在传统的结构分类数据库中的分类,表明论文提出的方法能够有效地对蛋白质三维结构进行定量比较.     

基于自适应粒子群算法和支持向量机的控制图模式识别

针对目前多品种、复杂化的生产趋势,提出了一种基于自适应变异的粒子群算法（AMPSO）和支持向量机（SVM）的控制图失效模式识别的方法。利用SVM小样本学习能力,设计一对一的SVM多分类器进行控制图模式识别,并利用AMPSO算法优化SVM核函数的参数。通过对10种控制图模式(6种基本模式和4种混合模式)的20维特征仿真数据对该方法进行检验,并通过与BP、SVM、PSO SVM识别方法的对比分析。仿真试验表明该方法有效提高了控制图模式的识别精度,达到9814%,而BP仅有75%,为控制图在线实时识别提供了一种可行的途径。      

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a two-layer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.     

基于PSO算法的模糊PSVM及其在旋转机械故障诊断中的应用

研究了粒子群优化改进的模糊线性PSVM在旋转机械故障诊断的应用。常规的PSVM对噪声或野值敏感,模糊PSVM可以很好的解决这种问题;对于非平衡样本,PSVM分类面会偏重于数据点较多的一类,从而降低正确分类性能,通过为不同样本分别设计不同的惩罚因子,提高分类器性能;模糊线性PSVM分类器的惩罚因子采用经典粒子群优化算法进行优化,避免传统方法对初始点和样本的依赖。通过旋转机械故障分类应用实例进行了设计方法的验证,首先对振动信号进行滤波,然后以不同频率频谱的谱峰能量作为FLPSVM分类器的输入特征参数,用于区分旋转机械的5种典型故障,试验结果表明了方法的有效性。     

Analysis of groundwater quality using fuzzy synthetic evaluation

This paper reports the application of fuzzy set theory for decision-making in the assessment of physico-chemical quality of groundwater for drinking purposes. Methodology based on fuzzy set theory used to express the quality of water in the imprecise environment of monitored data and prescribed limits given in a non-probabilistic sense. Fuzzy synthetic evaluation model gives the certainty levels for the acceptability of the water based on the prescribed limit of various regulatory bodies quality class and perception of the experts from the field of drinking water quality. Application of fuzzy rule based optimization model is illustrated with 42 groundwater samples collected from the 15 villages of Ateli block of southern Haryana, India. These samples were analysed for 16 different physico-chemical water quality parameters. Ten parameters were used for the quality assessment using this approach. The analysis showed that four samples were in "desirable" category with certainty level of 35-58%, 23 samples were in "acceptable" category whose certainty level ranged from 37 to 75% and remaining 15 samples were in "not acceptable" category for drinking purposes with certainty levels from 44 to 100%. This concludes that about 64% water sources were either in "desirable" or "acceptable" category for drinking purposes.     

基于模糊c均值聚类算法的控制图模式识别

控制图模式识别能够区分制造过程中的一般因素与异常因素,提高制造过程中的产品质量,减少成本,提高效益。利用蒙特卡洛方法产生样本;采用一维离散小波变换处理原始数据;利用模糊c均值聚类算法进行控制图模式识别。识别准确率99.43%,其标准差为0.002 8。这表明基于该方法的控制图模式识别准确率高,稳定性好,较现有的控制图模式识别方法具有简易、高效等特点。     

Automatic identification of bird targets with radar via patterns produced by wing flapping.

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.     

旋转机械振动故障诊断的一种模糊神经网络方法研究

介绍了一种基于多层感知器的模糊神经网络分类器，并针对其在旋转机械故障诊断中的应用，研究了网络构造过程中输入和输出模糊化的问题。文中利用振动频谱特征就旋转机械中几种典型的故障模式，采用模糊神经网络方法作了识别，且将其与传统的ＢＰ网络及模糊诊断方法进行了比较。研究结果表明：将模糊神经网络方法应用于旋转机械工况识别是有效的，它在处理分类边界模糊的数据时比传统的ＢＰ网络和模糊诊断方法具有更大的优越性。     

基于随机森林的统计控制图模式识别研究

引入随机森林方法进行统计控制图模式识别的研究。提取了控制图的统计特征和形状特征,设计了5种不同的特征组合方法,利用蒙特卡洛仿真方法产生训练数据集和测试数据集,选取了常用的3种模式识别方法（支持向量机方法、人工神经网络方法、决策树方法）进行对比。实验结果表明,随机森林方法相比其他3种分类器方法,在分类准确率和消耗时间两个维度上都有明显优势,可以应用于统计过程控制图模式识别。     

Classification of cultivation locations of Panax quinquefolius L samples using high performance liquid chromatography-electrospray ionization mass spectrometry and chemometric analysis

Panax quinquefolius L ( P. quinquefolius L) samples grown in the United States and China were analyzed with high performance liquid chromatography-mass spectrometry (HPLC-MS). Prior to classification, the two-way data sets were subjected to pretreatment including baseline correction and retention time (RT) alignment. Principal component analysis (PCA) and projected difference resolution (PDR) metrics were used to evaluate the data quality and the pretreatment effects. A fuzzy rule-building expert system (FuRES) classifier was used to classify the P. quinquefolius L samples grown in the United States and China with the optimized partial least-squares (o-PLS) classifier as the positively biased control method. A classification rate as high as 98 ± 3% with FuRES was obtained after baseline correction and RT alignment, which is equivalent to the result obtained by using the positively biased o-PLS control method (98 ± 3%). RT alignment improved the classification rates for both FuRES and o-PLS classifiers (18% improvement for the FuRES classification rate and 10% improvement for the o-PLS classification rate with baseline correction). From the rule obtained to classify the P. quinquefolius L samples grown in the United States and China, peaks were identified that can be prospective biomarkers for differentiating samples from different growth regions. HPLC-MS with chemometric analysis has the potential to be used as an authentication method for P. quinquefolius L grown in China and the United States.