格值树自动机与格值上下文无关树文法的等价性

本文将模糊树自动机和模糊上下文无关树文法的概念推广到格半群上。证明了在接受语言和生成语言的意义下,树自动机和上下文无关树文法是等价的。同时给出了构造正规形式的等价文法的方法。     

格值交替树自动机

交替（树）自动机因其本身关于取补运算的简洁性及其与非确定型（树）自动机的等价性,成为自动机与模型检测领域研究的一个新方向.在格值交替自动机与经典交替树自动机概念的基础上,引入格值交替树自动机的概念,并研究了格值交替树自动机的代数封闭性和表达能力.首先,证明了对格值交替树自动机的转移函数取对偶运算,终止权重取补之后所得自动机与原自动机接受语言互补这一结论.其次,证明了格值交替树自动机关于交、并运算的封闭性.最后,讨论了格值交替树自动机和格值树自动机、格值非确定型自动机的表达能力;证明了格值交替树自动机与格值树自动机的等价性,并给出了二者相互转化的算法及其复杂度分析;同时,提供了用格值非确定型自动机来模拟格值交替树自动机的方法.     

模糊Büchi自动机的等价刻画

模糊语言的研究是形式语言研究的焦点之一,然而如何对模糊语言进行刻画甚至更好地分类是其中一个重要研究方向.文章在模糊ω-语言的研究基础上,从模糊逻辑角度研究了模糊ω-正则语言的等价刻画.首先借助广义子集构造方法,证明了任一模糊Büchi自动机与具有分明初始状态和状态转移函数且具有模糊终状态的模糊Büchi自动机是等价的,藉此研究了模糊ω-正则语言的代数刻画和层次刻画,讨论了模糊ω-正则语言关于正则运算的封闭性;其次引入单体二阶(L)ukasiewicz逻辑的概念,给出模糊Büchi自动机识别语言的等价逻辑刻画;最后通过引入ω-星自由和ω-非周期模糊ω-语言,利用“层次化”处理技巧得到了多值逻辑意义下的分类定理,对模糊ω-正则语言给出了一种分类方法.     

采用树自动机推理技术的信息抽取方法

提出了一种利用改进的k-contextual树自动机推理算法的信息抽取技术。其核心思想是将结构化（半结构化）文档转换成树,然后利用一种改进的k-contextual树（KLH树）来构造出能够接受样本的无秩树自动机,依据该自动机接收和拒绝状态来确定是否抽取网页信息。该方法充分利用了网页文档的树状结构,依托树自动机将传统的以单一结构途径的信息抽取方法与文法推理原则相结合,得到信息抽取规则。实验证明,该方法与同类抽取方法相比,样本学习时间以及抽取所需时间上均有所缩短。     

基于树自动机的XQuery子类型关系和值-域关系

XQuery是W3C提出的一种对XML结构文档或数据进行查询的语言规范,该语言规范对其类型系统仅做了功能性的描述,而没有涉及在编译实现中遇到的两个类型之间子类型关系和值-域关系的判断方法.本文描述和分析了这两种关系,基于树自动机,给出了XQuery类型系统中子类型关系和值-域关系的判断算法.     

基于Unsharp量子逻辑的自动机和文法理论

初步建立了具有某种分配律的扩展格序效应代数和格序QMV代数这两种unsharp量子结构上的自动机与文法理论的基本框架。引入了ε-值正则文法的概念,证明了任意ε-值自动机识别的语言等价于某种ε-值正则文法所生成的语言;反之,任意[ε]-值正则文法所生成的语言等价于某种ε-值自动机识别的语言。讨论了ε-值正则语言在和、连接及反转运算下的封闭性质。     

模糊识别器与有穷自动机的等价性

针对模糊识别器与有穷自动机的关系,证明了当输入字母表相同时,任给一个模糊识别器,必然存在一个有穷自动机,使得模糊识别器的行为与有穷自动机所接受的语言相同;反之,任给一个有穷自动机,必然存在一个模糊识别器,使得有穷自动机所接受的语言与模糊识别器的行为相同,从而得出它们之间的等价性。     

扰动值模糊有限自动机及其语言*

引入扰动值模糊有限自动机及其语言的概念,讨论扰动值模糊有限自动机的状态转移函数的扩张问题,证明3类确定型扰动值模糊有限自动机、非确定型扰动值模糊有限自动机相互等价性,研究扰动值模糊有限自动机的语言关于正则运算的封闭性.     

基于语义攻击树的安全事件建模与分析

本文在安全事件的时间语义及上下文语义关系分析的基础上,建立语义攻击树模型,提出了增强非确定有限树自动机处理策略及其算法,并建立了事件检测系统框架.     

基于无秩树自动机的信息抽取技术研究

针对目前基于网页结构的信息抽取方法的缺陷,提出了一种基于无秩树自动机的信息抽取技术,其核心思想是通过将结构化(半结构化)文档转换成无秩树,然后利用(k,l)-contextual树构造样本自动机,依据树自动机接收和拒绝状态来对网页进行数据的抽取.该方法充分利用结构,依托树自动机将传统的以单一结构途径的信息抽取方法与文法推理原则相结合,得到信息抽取规则.实验结果表明,该方法与同类抽取方法相比在准确率、召回率以及抽取所需时间上均有所提高.     

Intuitionistic fuzzy recognizers and intuitionistic fuzzy finite automata

Intuitionistic fuzzy recognizers and intuitionistic fuzzy finite automata are discussed. The notions of intuitionistic fuzzy recognizer, complete accessible intuitionistic fuzzy recognizer, intuitionistic fuzzy finite automata, deterministic intuitionistic fuzzy finite automata, and intuitionistic fuzzy language are introduced. It is shown that the languages recognized by intuitionistic fuzzy recognizer are regular, and the intuitionistic fuzzy languages recognized by the intuitionistic fuzzy finite automaton and the intuitionistic fuzzy languages recognized by deterministic intuitionistic fuzzy finite automaton are equivalent. This work is supported by National Science Foundation of China (Grant No.10571112), “TRAPOYT” of China and National 973 Foundation Research Program(Grant No.2002CB312200).     

A multidimensional approach to syntactic pattern recognition

This paper describes a syntactic method for representing the primitive parts of a pattern as nodes of a type of directed graph. A linear representation of the digraph can then be presented to a regular unordered tree automaton for classification. Regular unordered tree automata can be simulated by deterministic pushdown automata, so this procedure can be implemented easily. Regular u-tree automata and the corresponding generative systems, regular u-tree grammars are formally defined. Several results are shown which are applicable to all syntactic pattern recognition schemes involving the use of primitives.     

A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets

In the past, many algorithms were proposed to adopt fuzzy-set theory for discovering fuzzy association rules from quantitative databases. The fuzzy frequent pattern (FFP)-tree and the compressed fuzzy frequent pattern (CFFP)-tree algorithms were respectively proposed to mine the incomplete fuzzy frequent itemsets from the tree-based structures. In the past, multiple fuzzy frequent pattern (MFFP)-tree algorithm was proposed to keep more linguistic terms for mining fuzzy frequent itemsets. Since the MFFP-tree algorithm inherits the property of the FFP-tree algorithm, numerous tree nodes are thus required to build the MFFP-tree structure for mining the desired multiple fuzzy frequent itemsets. In this paper, the compressed multiple fuzzy frequent pattern (CMFFP)-tree algorithm is designed to keep not only the linguistic term with maximum membership value but also the other frequent linguistic terms for mining the completely fuzzy frequent itemsets. In the designed CMFFP-tree algorithm, the multiple frequent linguistic terms are sorted in descending order of their occurrence frequencies to build the CMFFP-tree structure. The construction process is the same as the CFFP-tree algorithm except more information are kept for later mining process to discover the completely fuzzy frequent itemsets. Each node in the CMFFP-tree uses the additional array to keep the membership values of its prefix path by intersection operation. A CMFFP-mine algorithm is also designed to efficiently mine the multiple fuzzy frequent itemsets from the developed CMFFP-tree structure. Experiments are then conducted to show the performance of the proposed CMFFP-tree algorithm in terms of execution time and the number of tree nodes, compared to those of the MFFP-tree and CFFP-tree algorithms.     

On regular tree languages and deterministic pushdown automata

The theory of formal string languages and of formal tree languages are both important parts of the theory of formal languages. Regular tree languages are recognized by finite tree automata. Trees in their postfix notation can be seen as strings. This paper presents a simple transformation from any given (bottom-up) finite tree automaton recognizing a regular tree language to a deterministic pushdown automaton accepting the same tree language in postfix notation. The resulting deterministic pushdown automaton can be implemented easily by an existing parser generator because it is constructed for an LR(0) grammar, and its size directly corresponds to the size of the deterministic finite tree automaton. The class of regular tree languages in postfix notation is a proper subclass of deterministic context-free string languages. Moreover, the class of tree languages which are in their postfix notation deterministic context-free string languages is a proper superclass of the class of regular tree languages.     

Closure properties of linear context-free tree languages with an application to optimality theory

Context-free tree grammars, originally introduced by Rounds [Math. Systems Theory 4(3) (1970) 257–287], are powerful grammar devices for the definition of tree languages. The properties of the class of context-free tree languages have been studied for more than three decades now. Particularly important here is the work by Engelfriet and Schmidt [J. Comput. System Sci. 15(3) (1977) 328–353, 16(1) (1978) 67–99]. In the present paper, we consider a subclass of the class of context-free tree languages, namely the class of linear context-free tree languages. A context-free tree grammar is linear, if no rule permits the copying of subtrees. For this class of linear context-free tree languages we show that the grammar derivation mode, which is very important for the general class of context-free tree languages, is immaterial. The main result we present is the closure of the class of linear context-free tree languages under linear frontier-to-root tree transduction mappings. Two further results are the closure of this class under linear root-to-frontier tree transduction mappings and under intersection with regular tree languages.     

随机模糊故障树分析方法及应用

针对现有故障树分析模型无法解决复杂环境下的多重不确定性问题,利用随机模糊变量的本原机会概念,提出了随机模糊故障树顶事件本原机会的求解算法。该算法的基本思想是先通过模糊模拟产生一组符合要求的模糊数,然后利用这些模糊数做参数进行随机模拟求得顶事件的本原机会。最后,通过实例证明了该算法在解决双重不确定方面比任何模糊故障树算法都有优势。     

基于负载均衡的模糊概念并行构造算法

提高模糊概念格直接构造效率是形式概念分析领域的主要问题之一,而当前基于模糊伽罗瓦联系的闭包运算仍是构造模糊概念的主要计算负荷,为此,提出一种基于负载均衡的并行构造模糊概念算法.该算法使用树状结构组织,遍历由自然数区间简化的搜索空间,逐级并行产生模糊概念、缩减搜索区间,并通过重新划分子搜索空间,实现各个计算节点负载均衡.实验结果表明,所提出的算法在稀疏数据集上表现优秀,能够有效地提高模糊概念构造效率.     

一种新的关联特征和模糊聚类的进化树构建方法*

利用三联体和单联核苷酸的联合概率分布的差异来表示序列之间的差异,提出了一种新的关联特征TBC;对TBC特征矩阵进行平移极差变换,利用指数切比雪夫距离法构建了模糊相似矩阵,采用模糊聚类中的传递闭包法构建进化树。该方法不需要多序列比对,计算简单。对两组基因组序列构建进化树,实验结果验证了该方法的有效性。