模糊聚类与模糊识别理论模型研究

指出文 [1]模糊聚类与模糊识别模型的局限性 ,进一步研究模糊环境下的目标函数 ,修改模糊聚类中心的计算公式 ,提出计算权重的一个公式 ,拓广模糊聚类与模糊识别理论模型的应用范围     

可变模糊集合理论与可变模型集

在对立模糊集定义基础上给出以相对隶属函数表示的模糊可变集合定义,给出可变模糊聚类迭代模型、可变模糊模式识别模型、可变模糊对立识别模型.它们是可变模糊聚类、识别、优选决策、评价相统一的理论模型集,是可变模糊集的基础模型与核心内容,可用于自然、管理、人文、社会等各种学科中关于模糊聚类、识别、优选决策、评价、预测等众多实际领域.     

土壤分类的模糊集成算法与应用

土壤是一个多性状的连续体,其分类的首选方法是模糊聚类分析.但是模糊聚类分析中现有的基于模糊等价关系的动态聚类法和模糊c-均值法各有利弊,采用其中一种方法聚类肯定存在不足.为此集成两种聚类方法的优点,避其缺点,提出了用基于模糊等价关系的动态聚类方法和方差分析方法确定聚类数目和初始聚类中心,再用模糊c-均值法决定最终分类结果的集成算法,并将其应用到松花江流域土壤分类中,得到了较为切合实际的分类结果.     

基于模糊距离总变差的模糊识别模型及径流预报应用

根据待识别样本的加权广义欧氏权距离,基于模糊距离总变差为最小的目标函数,提出了一种新的计算模糊聚类中心矩阵、模糊模式识别矩阵与目标权重的模糊识别模型.该模型具有较好的稳定性,径流中长期预报实例说明是可行的.     

基于随机采样模糊聚类和卡尔曼滤波方法的模糊辨识

提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .     

弹体分离识别的模糊聚类算法

天地基联合预警中的引导交接问题是导弹预警成功的关键,通过分析弹体分离多目标飞行特性,构建弹头及伴飞物空间位置和速度的模糊等价关系,并基于空间点共线及对称性的模糊判定方法,提出基于模糊聚类识别弹头的一种新算法,解决在弹头伴飞物中准确识别出弹头的问题。仿真计算结果表明,方法具有较高的准确率和很好的实用价值。     

区间模糊ISODATA动态聚类算法

目前模糊技术已经应用于许多智能系统,如模糊关系与模糊聚类.聚类是数据挖掘的重要任务,它将数据对像分成多个聚类,在同一个聚类中,对象的属性特征之间具有较高的相似度,有很大研究及应用价值.结合数据库中的挖掘技术,对属性特征为区间数的多属性决策问题,提出了一种基于区间数隶属度的区间模糊ISODATA动态聚类方法.     

一种稳健的聚类方法

本文讨论一种新的聚类方法 :属性均值聚类 .通过理论分析 ,属性均值聚类是比模糊均值聚类更稳健的聚类方法 .数值实验说明了该方法的有效性     

基于自适应模糊聚类的T-S模糊辨识方法

针对模糊建模在进行结构辨识时需事先设定聚类数的问题,本文在改进模糊分割聚类算法的基础上,对算法中聚类数c给出优选方法,提出了参数自适应模糊聚类算法,并结合递推最小二乘法构建T-S模糊辨识算法。为了验证本文提出的模糊辨识方法的有效性,采用该算法对熟知的Box-Jenkins煤气炉数据和实际的电液位置伺服系统数据进行建模,结果显示该辨识方法具有较高的逼近精度和较好的泛化能力。     

模糊识别、决策与聚类理论模型

本文首先建立了一个新的模糊模式识别理论模型。然后提出新的模糊综合决策与模糊聚类模型,文中也指出了ISODATA聚类方法存在的不足。     

保障用户认证安全的判别最大熵语音识别机制

为了提高电子商务中用户认证的安全性,提出了一种高识别率的判别最大熵语音识别机制DME.该语音识别方法同时考虑语音与语言两方面的因素,并将语音和语言特征进行有效地结合,在统一的最大熵模型下,实现判别训练,确保观察样本能正确地分配到其对应状态,以提高所训练出的语音模型的正确识别率.详细的实验及与现有方法的比较结果表明,对不同环境下的语音数据,提出的语音识别方法具有更好的识别性能,对提高电子商务中用户认证系统的安全性能具有理论与实际意义.     

Logical correctors in the problem of classification by precedents

The problem of recognition (classification) by precedents is considered. Issues of improving the recognition ability and the training rate of logical correctors, i.e., the recognition procedures based on the construction of correct sets of elementary classifiers, are studied. The concept of a correct set of generic elementary classifiers is introduced and used to construct and investigate a qualitatively new model of the logical corrector. This model uses a wider class of correcting functions than in the earlier constructed models of logical correctors.     

隐马尔可夫模型在语音识别中的应用

隐马尔可夫模型 (HMM)的基本技术是语音识别中较为成功的算法 .主要是它具有较强的对时间序列结构的建模能力 .本文首先深入浅出地介绍了 HMM的基本技术和一个基于 HMM的孤立词语音识别系统的构成方法 ,其次 ,基于 HMM尚存有一些缺陷 ,造成语音识别能力较弱 ,为此本文又进一步阐述了语音识别应用中的几种改进的 HMM系统及目前的热点方法—— HMM与 ANN构成的混合网络     

Minimum classification error training in example based speech and pattern recognition using sparse weight matrices

The Minimum Classification Error (MCE) criterion is a well-known criterion in pattern classification systems. The aim of MCE training is to minimize the resulting classification error when trying to classify a new data set. Usually, these classification systems use some form of statistical model to describe the data. These systems usually do not work very well when this underlying model is incorrect. Speech recognition systems traditionally use Hidden Markov Models (HMM) with Gaussian (or Gaussian mixture) probability density functions as their basic model. It is well known that these models make some assumptions that are not correct. In example based approaches, these statistical models are absent and are replaced by the pure data. The absence of statistical models has created the need for parameters to model the data space accurately. For this work, we use the MCE criterion to create a system that is able to work together with this example based approach. Moreover, we extend the locally scaled distance measure with sparse, block diagonal weight matrices resulting in a better model for the data space and avoiding the computational load caused by using full matrices. We illustrate the approach with some example experiments on databases from pattern recognition and with speech recognition.     

Classification Modulo Invariance,With Application to Face Recognition

This article presents techniques for constructing classifiers that combine statistical information from training data with tangent approximations to known transformations; it demonstrates the techniques by applying them to a face recognition task. Our approach is to build Bayes classifiers with approximate class-conditional probability densities for measured data. The high dimension of the measurements in modern classification problems such as speech or image recognition makes inferring probability densities from feasibly sized training datasets difficult. We address the difficulty by imposing severely simplifying assumptions and exploiting a priori information about transformations to which classification should be invariant. For the face recognition task, we used a five-parameter group of such transformations consisting of rotation, shifts, and scalings. On the face recognition task, a classifier based on our techniques has an error rate that is 20% lower than that of the best algorithm in a reference software distribution.     

最优化方法任分子识别中的应用

分子识别是指两个或多个分子靠非共价键专一地结合在一起．分子识别在物质的形成,细胞信号的传递,基因信息的表达和药物的设计等方面起重要的作用．我们首先对数学和计算方法在分子识别上的应用作了回顾,并从量子力学,经典分子力学和热力学上解释分子识别可转换成一类最优化问题．其次,我们指出了解决这类最优化问题的困难．最后,我们报告了在预报配体与蛋白质识别上所获得的一类新的选择方法．这类方法可将预报的成功率从63％提高到90％．     

Statistical methods in multi-speaker automatic speech recognition

Automatic speech recognition and understanding (ASR) plays an important role in the framework of man-machine communication. Substantial industrial developments are at present in progress in this area. However, after 40 years or so of efforts several fundamental questions remain open. This paper is concerned with a comparative study of four different methods for multi-speaker word recognition: (i) clustering of acoustic templates, (ii) comparison with a finite state automaton, (iii) dynamic programming and vector quantization, (iv) stochastic Markov sources. In order to make things comparable, the four methods were tested with the same material made up of the ten digits (0 to 9) pronounced four times by 60 different speakers (30 males and 30 females). We will distinguish in our experiments between multi-speaker systems (capable of recognizing words pronounced by speakers that have been used during the training phase of the system) and speaker-independent systems (capable of recognizing words pronounced by speakers totally unknown to the system). Half of the corpus (15 male and 15 female) were used for training, and the remaining part for test.     

层次分析法在身份鉴别方案选择中的应用

针对身份鉴别方案比较的问题,采用层次分析法AHP(Analytic HierarchyProcess)建立了身份鉴别方案比较的数学模型,定量分析了指纹识别、人脸识别、语音识别、虹膜识别、静脉红外识别和DNA识别六种生物识别技术在身份鉴别中的综合表现,并做出评价.评价结果对重要场合安全检查中人员身份鉴别方法的选择有一定的指导意义.