基于相似粗糙集的案例特征权值确定新方法

针对现有案例特征权值确定方法客观性差、算法复杂等问题，首先介绍和完善了基于传统粗糙集的权值确定方法．其次，针对基于传统粗糙集的方法会造成案例相似度测量误差从而影响案例推理的准确性的问题，将传统粗糙集的不可分辨关系推广为相似关系，提出了一种基于相似粗糙集的案例特征权值确定方法．给出了相似粗糙集的基本定义，以及利用该方法基于差别矩阵进行特征权值计算的两个定理．最后，用实例表明了方法的有效性．     

两种改进的相似度及模糊推理方法

在专家系统的模糊推理中,基于相似度的方法是一种简便而又重要的方法。文章指出了已有的两种相似度及其算法的局限性,提出了两种改进的相似度及其推理算法,并用实例说明了改进方法的效果。     

基于案例推理的交通疏导辅助决策方法

为避免实际交通疏导问题中人为主观因素的影响,将案例推理理论应用于交通疏导决策中,提出一种基于案例推理的交通疏导决策方法,建立交通疏导案例库模型。区分属性数据类型,设计基于ID 3信息熵的案例相似度算法,案例匹配时可以兼顾特征属性与案例效果,避免传统算法中属性值缺失的问题。实例验证了该方法能够提高案例推理结果的准确度,对辅助处理交通疏导问题具有一定实际指导意义。     

融合多重实例关系的无监督跨模态哈希检索

大多数跨模态哈希检索方法仅使用余弦相似度进行特征匹配,计算方式过于单一,没有考虑到实例的关系对于性能的影响.为此,提出一种基于多重实例关系图推理的方法,通过构造相似度矩阵,建立全局和局部的实例关系图,充分挖掘实例之间的细粒度关系.在多重实例关系图的基础上进行相似度推理,首先分别进行图像模态和文本模态关系图内部的推理,然后将模态内的关系映射到实例图中进行推理,最后执行实例图内部的推理.此外,为了适应图像和文本两种模态的特点,使用分步训练策略训练神经网络.在MIRFlickr和NUS-WIDE数据集上实验表明,提出的方法在mAP指标上具有很明显的优势,在Top-k-Precision曲线上也获得良好的效果.这也说明所提方法对实例关系进行深入挖掘,从而显著地提升检索性能.     

基于Ontology的专家系统研究

针对当前专家系统领域知识语义表示不精确及推理效率低的问题,提出一种基于Ontology的专家系统框架。根据规则结构建立规则模式Ontology和动作Ontology,将用户推理条件作为模式子Ontology,设计出基于名称和实例的相似度计算及Clips推理机制的专家系统推理算法。结合高性能切削加工应用背景给出一个应用实例,应用结果表明,该系统能够明显提高推理效率和推理结果的语义表达。     

基于子图同构与事例匹配的装配体局部结构相似性分析

基于事例推理是解决面向装配的设计和装配工艺规划问题的有效方法,装配体检索是应用此方法的关键步骤.为此提出一种基于子图同构与事例匹配的装配体局部结构相似性分析方法.该方法将装配体零件间的装配关系用属性邻接图来表示,通过对装配体中联结关系的定性分类及装配体属性邻接图预处理规则的定义,利用Ullmann算法匹配出相似的装配体图(子图)结构,并给出了装配体局部结构相似度计算公式.最后通过实例验证了文中方法的有效性.     

轴类件耦合神经网络实例推理CAPP索引模型的研究

本文提出了一种基于神经网络的工艺设计实例推理索引模型。与现存大多数实例推理系统不同该方法用神经网络实现实例的动态分类和索引。实例层次分类的三层结构和基于特征的聚类模板概念，为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件。该方法的优点在于实例的高速、有效检索，知识获取的简化以及基于神经网络的检索算法的鲁棒性。     

基于粗糙集的不完备信息系统规则推理算法

定义了非对称相似关系的近似集概念，提出了一种利用非对称相似关系下近似集和属性值对的基于粗糙集的确定规则推理算法．该算法无需改变初始不完备信息系统的结构，能直接处理缺省数据．实验结果表明，所获得的确定决策规则简洁、高效，与缺省值无关．     

基于实例推理的高速切削数据库系统构建

针对高速切削领域缺乏完整、实用的数据库支持的问题,构建了一种发动机零件高速切削工艺实例库模型,提出了一种实现实例推理的算法.研究了发动机零件的切削工艺,归纳了常规切削中对切削工艺具有重要影响作用的工艺特征,对常规切削进行问题描述;在此基础上,从提高加工精度的角度出发,归纳了高速切削工艺中对提高加工质量具有重要影响作用的工艺特征,提出了高速切削问题描述的方法;针对各个工艺特征给出了局部相似度的计算方法,提出了一种客观赋权法即熵值法来确定各个工艺特征的权重值;采用最近邻居法实现实例检索.通过实际应用证明了这种数据库的可行性以及实例推理算法的合理性.     

基于案例推理的应急辅助决策方法研究*

将案例推理的方法应用到应急决策中,为应急决策提供了一种实用的科学辅助方法。在分析了应急案例特征的基础上设计了一种基于概念树—突发事件本体模型—事件元模型三层架构的应急案例通用的案例描述与组织方法;根据应急案例属性复杂及属性值缺失的问题设计了基于结构相似度和属性相似度双层结构的案例全局相似度计算算法,避免了传统最近相邻算法中的属性值缺失问题;最后通过基于案例推理的应急辅助决策原型系统的开发使设计方法得以实现,证明了该方法具有较好的实用性。     

Information fusion between short term learning and long term learning in content based image retrieval systems

Content based image retrieval (CBIR) systems could provide more precise results by taking the user’s feedbacks into account. Two types of the relevance feedback learning paradigms are short term learning (STL) and long term learning (LTL). By using both STL and LTL, a collaborative CBIR system is proposed in this paper. The proposed system introduced three fusion methods: including fusion in retrieved images, fusion in ranks, and fusion in similarities to make cooperation between STL and LTL. The proposed fusion methods are examined in a CBIR system equipped with a proposed statistical semantic clustering (SSC) method of LTL. The SSC method works based on the concept of semantic categories of the images by clustering techniques and constructing a relevancy matrix between images and semantic categories. The results of the SSC method with the suggested fusion methods are compared with two state-of-the-art LTL methods, namely virtual feature based method and dynamic semantic clustering. Comparative results confirm the efficiency of the proposed method. Furthermore, experimental results demonstrate that for a unique LTL method, various fusion methods lead to different results.     

Regularized margin-based conditional log-likelihood loss for prototype learning

The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is based on the discriminative model called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training as well as to maximize the hypothesis margin. The CLL in the LOGM algorithm is a convex function of margin, and so, shows better convergence than the MCE. In addition, we show the effects of distance metric learning with both prototype-dependent weighting and prototype-independent weighting. Our empirical study on the benchmark datasets demonstrates that the LOGM algorithm yields higher classification accuracies than the MCE, generalized learning vector quantization (GLVQ), soft nearest prototype classifier (SNPC) and the robust soft learning vector quantization (RSLVQ), and moreover, the LOGM with prototype-dependent weighting achieves comparable accuracies to the support vector machine (SVM) classifier.     

Discriminative Histogram Intersection Metric Learning and Its Applications

In this paper, a novel method called discriminative histogram intersection metric learning (DHIML) is proposed for pair matching and classification. Specifically, we introduce a discrimination term for learning a metric from binary information such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1) The histogram intersection strategy is adopted into metric learning to deal with the widely used histogram features effectively. 2) By introducing discriminative term and classification error term into metric learning, a more discriminative distance metric and a classifier can be learned together. 3) The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications: face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark, a dataset with more than 7 000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric learning, compared with the recent state-of-the-art approaches.     

基于自适应稀疏表达的并行磁共振重建方法

为精确地进行并行磁共振成像,文章利用字典学习的强大捕捉细节和稀疏开发能力,提出了一种基于自适应稀疏表达的重建方法。该方法将并行磁共振重建问题转化为最小化由字典学习和数据拟合项构成的目标函数,并采用了分而治之的方案求解未知变量。为验证其有效性,将该方法与目前主流的两种方法在人体实际磁共振数据上进行了测试。测试结果显示,文章提出的方法能在抑制图像噪声的同时较好地保存图像细节。     

一种基于聚类的PU主动文本分类方法

文本分类是信息检索的关键问题之一.提取更多的可信反例和构造准确高效的分类器是PU(positive andunlabeled)文本分类的两个重要问题.然而,在现有的可信反例提取方法中,很多方法提取的可信反例数量较少,构建的分类器质量有待提高.分别针对这两个重要步骤提供了一种基于聚类的半监督主动分类方法.与传统的反例提取方法不同,利用聚类技术和正例文档应与反例文档共享尽可能少的特征项这一特点,从未标识数据集中尽可能多地移除正例,从而可以获得更多的可信反例.结合SVM 主动学习和改进的Rocchio 构建分类器,并采用改进的TFIDF(term frequency inverse document frequency)进行特征提取,可以显著提高分类的准确度.分别在3 个不同的数据集中测试了分类结果(RCV1,Reuters-21578,20 Newsgoups).实验结果表明,基于聚类寻找可信反例可以在保持较低错误率的情况下获取更多的可信反例,而且主动学习方法的引入也显著提升了分类精度.     

Adaptive iterative learning control for switched nonlinear continuous-time systems

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.     

基于单神经元的参数自学习模糊控制器的研究

为进一步改善永磁交流伺服系统的动静态性能,该文设计了一种基于单神经元的参数自学习模糊控制器,它在控制规则数与二维控制器相当的基础上,可实现三维模糊控制的效果。引入的单神经元采用改进的BP算法来实现比例因子的在线自学习。控制器具有结构及算法简单、易于解析实现的特点。为验证其有效性,该文通过仿真试验,将其与采用常规的PI调节器的控制系统进行比较,结果表明,这种模糊控制器具有较好的控制效果。     

基于子空间学习的图稀疏属性选择算法*

针对处理高维度属性的大数据的属性约减方法进行了研究。发现属性选择和子空间学习是属性约简的两种常见方法,其中属性选择具有很好的解释性,子空间学习的分类效果优于属性选择。而往往这两种方法是各自独立进行应用。为此,提出了综合这两种属性约简方法,设计出新的属性选择方法。即利用子空间学习的两种技术(即线性判别分析(LDA)和局部保持投影(LPP)),考虑数据的全局特性和局部特性,同时设置稀疏正则化因子实现属性选择。基于分类准确率、方差和变异系数等评价指标的实验结果比较,表明该算法相比其它对比算法,能更有效的选取判别属性,并能取得很好的分类效果。     

基于局部类内结构的鉴别性字典学习方法

针对支持向量引导的字典学习(support vector guided dictionary learning,SVGDL)的鉴别约束项只体现了大间隔原理,而没有很好地利用数据空间内在结构信息的问题,提出了一种新颖的鉴别性字典学习方法——基于局部类内结构的鉴别性字典学习方法。该方法结合了大间隔原理和局部Fisher线性鉴别分析作为鉴别约束条件来指导指点学习。通过建立一个局部类内散度矩阵来编码数据空间的分布结构,增强了挖掘同类数据空间局部结构的能力并进一步地表示了编码向量在数据空间中的局部相似性。为了评价提出方法在图像识别上的表现,在几个常见图像数据集上进行了实验。结果表明,提出方法与大间隔方法相比,在平均识别率上有着明显的提高。     

Repetitive control of electrically driven robot manipulators

This article presents a novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory. We propose a novel model, which presents the highly non-linear dynamics of robot manipulator in the form of linear discrete-time time-varying system. Based on the proposed model, we develop a two-term control law. The first term is an ordinary time-optimal and minimum-norm (TOMN) control by employing parametric controllers to guarantee stability. The second term is a novel robust control to improve the control performance in the face of uncertainties. The robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances. Performance of the proposed method is compared with two discrete methods, namely the TOMN control and an adaptive iterative learning (AIL) control. Simulation results confirm superiority of the proposed method in terms of the convergence speed and precision.