基于CnV结构的自动语言辨识研究

与说话人识别、连续语音识别相比,自动语言辨识是一个相对较新的研究,而且是一项较难的课题。与音素配位学相比较韵律是语言辨识的更有希望的一个语言辨识特征。论文介绍了一种基于伪音节结构CnV的自动语言辨识方法,该系统提取了辅音、元音构成的伪音节结构的MFCC和!MFCC特征参数,使用了与语言无关的GMM算法模型化该特征。经过对OGI-TS数据库中的英语、法语、汉语测试表明,元音、辅音特征信息在语言辨识中起到一定作用,伪音节结构模型也是语言辨识的有效模型之一。     

模糊粗糙集的相似度量和相似性方向

粗糙集理论是一种新的处理模糊和不确定性知识的软计算工具,在人工智能及认知科学等众多领域已经得到了广泛的应用。相似度量的研究是模糊集理论与粗糙集理论的热点问题之一。文章提出了一种更精确、更合理的相似度量方法,讨论了它的一些性质。然后,在此基础上提出了模糊粗糙集的相似性方向的概念,用于比较两个相似的模糊粗糙集所包含信息的精确性大小,并给出了一个关于相似性方向的判别函数。这在近似推理、模式识别和决策分析等领域有着广泛的应用。最后,通过一个实例,分析说明了这种相似度量方法和相似性方向的判别方法是更合理更有效的。     

一个带有相似性关系的模糊逻辑

模糊集与模糊逻辑是处理模糊性与不确定性信息的重要数学工具,相似性关系是模糊集的一个基本概念。为了在模糊逻辑中集成相似性关系并考虑其模糊推理,提出了一个带有相似性关系的模糊逻辑,给出了其语法及语义描述,在模糊谓词逻辑情形下,讨论并证明了基于归结与调解方法的模糊推理的有关属性,考虑到许多定理证明器和问题解决系统均是基于否证法,证明了归结与调解方法对模糊谓词演算的反驳完备性定理。     

基于Matlab的导弹弹道模糊控制系统仿真

该文建立起导弹弹道仿真的整体结构,设计了导弹纵向模糊控制系统,并详细讨论了利用Simulink工具箱和Fuzzy Logic工具箱进行模糊控制器的仿真。仿真结果表明,该控制系统具有较好的动态性能。     

基于模糊相似度量的图像检索

提出一种基于Sugeno模糊积分的模糊相似度量并用于图像检索中。文章用模糊测度来描述人的主观反馈。试验表明该文的方法可以大大提高图像检索系统的效率和稳定性,在反馈后的表现要优于加权平均方法(WAO)和采用Choquet积分(CI)的方法。     

基于正规三角模糊集相似度量的模糊推理

文章首先定义了正规模糊集相似度量的一般公式,然后给出了正规三角模糊集相似度量计算的简化公式,在正规三角模糊集相似度量的基础上,提出了一种新的模糊推理的方法——基于正规三角模糊集相似度量的模糊推理,并与Mamdani模糊推理方法做了比较。最后给出了模糊推理的仿真实现。     

基于模糊等价的毕达哥拉斯模糊集相似度构造方法

毕达哥拉斯模糊集是Zadeh模糊集的一种推广形式,其相似度刻画方法是毕达哥拉斯模糊集理论的重要研究内容.现有的毕达哥拉斯模糊集相似度大多针对具体问题而提出.为推广毕达哥拉斯模糊集理论的应用范围,文中基于模糊等价研究毕达哥拉斯模糊集相似度的一般构造方法.将模糊等价概念推广至毕达哥拉斯模糊数,提出了PFN(Pythagor...     

C语言与汇编语言编写的软件相似性鉴定

在软件相似性鉴定中,笔者遇到了一个C语言编写软件和汇编语言编写软件的相似性鉴定,在过往的几个相似判决案例中,鉴定机构都直接比对了目标代码,且在其中一份判决案例中直接提到了在二进制代码实质相同的情形下,存在用不同语言编写的源程序的可能性极小。而在本案中,经过试验求证,发现相同的目标代码是可能来源于不同语言编写的源代码,而且C语言源代码和汇编语言源代码之间存在转换关系,C语言源代码可在编译中生成汇编语言代码,而C语言源代码生成的目标代码也可反编译成汇编语言代码,从而进行进一步的比对,这种比对,在原被告都提供了与目标代码对应的源代码的情况下,比只比对目标代码显然更具有说服力。     

基于直觉模糊集的图像相似性度量

提出一种基于HSV颜色直方图的图像直觉模糊模型.在该模型下图像可看作是一个直觉模糊集合(IFS),图像之间的相似程度可通过计算直觉模糊集合之间距离来度量.实验数据表明:在HSV颜色空间下基于直觉模糊集的相似性度量能够有效用于图像数据库的查询,并且比普通基于模糊集的相似性度量和直方图距离在查询正确率方面提高5%～10%.     

基于模糊相似度的RPCL文本聚类算法

文本聚类过程中,存在着文本数据空间维数巨大,聚类的数目不能直接确定等问题。为此,有专家学者提出了次胜者受罚的竞争学习(Rival Penalized Competitive Learning)算法,简称RPCL算法。该算法在一定程度上,解决了聚类的数目的确定问题。但是,该算法只适合做低维数据的聚类,对于高维数据聚类效果极差。该文提出了一种改进的RPCL算法,该方法不再采用欧氏距离去计算相似度,而是采用模糊相似度的方法,通过实验表明,改进的RPCL算法在聚类效果上好于经典的RPCL算法。     

关于模糊粗糙集的相似度量

基于对不确定性信息处理的背景,定义了模糊粗糙值与模糊粗糙集的相似度量,研究了它们的有关性质。     

Fuzzy Diffusion Distance Learning for Cartoon Similarity Estimation

In this paper,a novel method called fuzzy diffusion maps (FDM) is proposed to evaluate cartoon similarity,which is critical to the applications of cartoon recognition,cartoon clustering and cartoon reusing.We find that the features from heterogeneous sources have different influence on cartoon similarity estimation.In order to take all the features into consideration,a fuzzy consistent relation is presented to convert the preference order of the features into preference degree,from which the weights are calculated.Based on the features and weights,the sum of the squared differences (L2) can be calculated between any cartoon data.However,it has been demonstrated in some research work that the cartoon dataset lies in a low-dimensional manifold,in which the L2 distance cannot evaluate the similarity directly.Unlike the global geodesic distance preserved in Isomap,the local neighboring relationship preserved in Locally Linear Embedding,and the local similarities of neighboring points preserved in Laplacian Eigenmaps,the diffusion maps we adopt preserve diffusion distance summing over all paths of length connecting the two data.As a consequence,this diffusion distance is very robust to noise perturbation.Our experiment in cartoon classification using Receiver Operating Curves shows fuzzy consistent relation’s excellent performance on weights assignment.The FDM’s performance on cartoon similarity evaluation is tested on the experiments of cartoon recognition and clustering.The results show that FDM can evaluate the cartoon similarity more precisely and stably compared with other methods.     

基于相似关系的模糊粗糙集模型

1引言 Rough集[1]是一种处理不完整和不确定信息的数学工具,目前,Rough集已被成功的应用于机器学习、决策分析、过程控制、模式识别与数据挖掘等领域.虽然Rough集理论对于不完备知识的处理是有效的,但是它对原始数据本身的模糊性缺乏相应的处理能力[2].     

Robust Stabilization of Uncertain Fuzzy Systems Using Variable Structure System Approach

Based on the variable structure system (VSS) theory, we develop a fuzzy control system design method for a class of uncertain nonlinear multivariable systems that can be represented by a Takagi-Sugeno fuzzy model. We make the first attempt to relax the restrictive assumption that each nominal local system model shares the same input channel, which is required in the traditional VSS-based fuzzy control design methods. As the local controller we use a sliding mode controller with a switching feedback control term. In terms of linear matrix inequalities (LMIs), we derive a sufficient condition for the existence of linear sliding surfaces guaranteeing asymptotic stability of the reduced-order equivalent sliding mode dynamics. We present an LMI characterization of such sliding surfaces. We also give an LMI-based algorithm to design the switching feedback control term so that a stable sliding motion is induced in finite time. Finally, we give a numerical design example.     

递推窗口式多层构造简化模糊推论模型的研究

提出了一种递推窗口式多层构造简化模糊推论模型,在该模糊模型中,因各层的分割数相异,而使得该模型规则具有不同特性,在其模糊分割数较多的层上,可以获得高辨识精度的模糊规则,而在分割数较少的层上,模糊规则的生态变得简单易行,该文通过对两个非线性函数的辩识实验,验证了多层构造简化模糊推论模型有效性     

软件可理解性结构及其模糊度量方法

为了得到软件可理解性度量的单一表示,提出了基于软件呵理解性结构的模糊度量方法。通过对软件可理解性组成部分的分析,得出了软件可理解性的层次结构,在此基础上建立了可理解性各个组成部分的模糊隶属度函数,最后通过算例介绍了计算软件可理解性的模糊度量方法。     

Design Pattern Detection Using Similarity Scoring

The identification of design patterns as part of the reengineering process can convey important information to the designer. However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues: identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method     

Credible Case-Based Inference Using Similarity Profiles

In this paper, we propose a method for retrieving promising candidate solutions in case-based problem solving. Our method, referred to as credible case-based inference, makes use of so-called similarity profiles as a formal model of the key hypothesis underlying case-based reasoning (CBR), namely, the assumption that similar problems have similar solutions. Proceeding from this formalization, it becomes possible to derive theoretical properties of the corresponding inference scheme in a rigorous way. In particular, it can be shown that, under mild technical conditions, a set of candidates covers the true solution with high probability. Thus, the approach supports an important subtask in CBR, namely, to generate potential solutions for a new target problem in a sound manner and hence contributes to the methodical foundations of CBR. Due to its generality, it can be employed for different types of performance tasks and can easily be integrated in existing CBR systems.     

利用分块相似系数构造感知图像Hash

提出一种基于图像分块相似系数的感知稳健图像Hash.先对图像预处理,再进行重叠分块,在密钥控制下,利用高斯低通滤波器生成伪随机参考图像块,分别计算每个分块与参考图像块的相关系数得到图像特征序列.依此将相邻两个分块特征值合并以缩短Hash长度,同时对压缩后的特征序列进行重排,进一步提高图像Hash的安全性.最后对归一化特征值进行量化,并运用Huffman方法对其编码,进一步压缩Hash长度.理论分析和实验结果表明,该图像Hash方法对JPEG压缩、适度的噪声干扰、水印嵌入、图像缩放以及高斯低通滤波等常见图像处理有较好的鲁棒性,能有效区分不同图像,冲突概率低,可用于图像篡改检测.     

Local Community Detection Using Link Similarity

Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis.As the complete information of network is often difficult to obtain,such as networks of web pages,research papers and Facebook users,people can only detect community structure from a certain source vertex with limited knowledge of the entire graph.The existing approaches do well in measuring the community quality,but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices.Moreover,they have predefined parameters which are difficult to obtain.This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex.Inspired by the fact that elements in the same community are more likely to share common links,we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community.A three-phase process is also used for the sake of improving quality of community structure.Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.