基于量子逻辑的下推自动机的代数刻画

首先,本文提出量子下推自动机（简记为L-VPDA）的概念,从代数角度出发详细研究了此类自动机的性质,同时建立此类自动机的代数刻画,即利用量子状态构造证明了任意L-VPDA与状态转移为经典函数且具有量子终状态的L-VPDA间的相互等价性;其次详细研究了量子上下文无关语言的代数刻画以及对于正则运算的封闭性。     

量化上下文无关语言的代数性质

通过引入量化下推自动机与量化上下文无关文法的定义,研究了以两种不同方式接受语言的量化下推自动机等价性问题,证明了在可交换的双幺赋值幺半群上,量化下推自动机接受的语言与量化上下文无关文法生成的语言相同。     

格值下推自动机与格值上下文无关文法

引入了格值下推自动机、格值上下文无关文法及它们的语言的概念,证明了格值下推自动机以两种不同方式接受的语言类的等价性,研究了格值Chomsky范式文法、格值上下文无关文法及其派生所产生的语言的等价条件,揭示了在一定条件下,格值下推自动机接受的语言类与格值上下文无关文法产生的语言类的等价性,证明了有理格值语言均被格值下推自动机识别。     

量子上下文无关文法的代数性质

提出了量子上下文无关文法（l-VCFG）的概念,并研究了其具有的代数性质;证明了量子上下文无关文法（l-VCFG）和Chomsky范式文法（l-VCNF）以及Greibach范式文法（l-VGNF）的相互等价性;详细研究了量子上下文无关语言的代数刻画以及对于正则运算的封闭性。     

基于量子逻辑的自动机和文法理论

初步建立了基于量子逻辑的自动机和文法理论的基本框架.引入了量子文法(称为l值文法),特别是证明了任意l值正规文法生成的语言(称为量子语言)等价于某种基于量子逻辑且含动作(的自动机(称为l值自动机)识别的语言,反之,任意l值自动机识别的语言等价于某l值正规文法生成的语言.建立了l值泵引理,并得到量子语言的判定性刻画.最后简要讨论了正规文法与量子文法(即l值正规文法)的关系.因此,为进一步研究更复杂的量子自动机(如量子下推自动机和Turing机)和量子文法(如量子上下文无关文法和上下文有关文法)奠定了基础.     

上下文无关语言的可重复序列及其性质

通过分析下推自动机的运行规律和特点,提出上下文无关语言的可重复序列的概念,将其划分为平衡重复序列、增重复序列、减重复序列三类;研究了这三类可重复序列在下推自动机的状态转换图中的结构表现和性质,通过分析下推自动机状态转换图中标注回路与可重复序列之间的关系,给出求解可重复序列的计算方法;证明了不同类型的可重复序列对上下文无关语言性质的影响,利用可重复序列揭示了上下文无关语言的Pumping引理的本质特征,并给出正规语言判定的一个充分必要条件.     

Lukasiewicz逻辑值上下文无关语言的代数刻画

提出了基于Lukasiewicz逻辑的下推自动机（l-VPDA）的概念,从代数角度研究了此类自动机的性质,同时建立此类自动机的代数刻画,即利用模糊状态构造,证明了任意以终状态方式接受模糊语言的l-VPDA与状态转移为经典函数且具有l值模糊终状态的l-VPDA间的相互等价性;并证明任意以空栈方式接受模糊语言的l-VPDA与状态转移除一步转移为模糊的以外,其余都是经典函数的l-VPDA是相互等价的;详细研究了l-值模糊上下文无关语言的代数和层次刻画,以及对于正则运算的封闭性。     

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。     

基于量子逻辑的确定型正则文法

基于量子逻辑的文法理论是量子计算模型的一个重要研究方向.给出了基于量子逻辑的确定型正则文法的概念,证明了基于量子逻辑的确定型正则文法与基于量子逻辑的确定型自动机的等价性.利用此等价性,给出了量子确定正则语言的代数刻画和层次刻画,并得到量子确定正则语言关于正则运算的封闭性.     

量子Müller自动机与单体二阶量子逻辑

给出量子Müller自动机（简称LVMA）的概念,通过引入量子有限步可识别语言和量子状态构造方法,证明了在量子逻辑意义下4类量子Müller自动机彼此相互等价.利用该等价性,建立了量子无穷正则语言的代数刻画和层次刻画,籍此研究了量子无穷正则语言关于无穷正则运算的封闭性.同时,给出了量子Müller自动机所识别语言的单体二阶逻辑描述,深化和推广了量子逻辑意义下的Büchi基本定理.     

Notes on automata theory based on quantum logic

The main results are as follows: (1) it deals with a number of basic operations (concatenation, Kleene closure, homomorphism, complement); (2) due to a condition imposed on the implication operator for discussing some basic issues in orthomodular lattice-valued automata, this condition is investigated in detail, and it is discovered that all the relatively reasonable five implication operators in quantum logic do not satisfy this condition, and that one of the five implications satisfies such a condition iff the truth-value lattice is indeed a Boolean algebra; (3) it deals further with orthomodular lattice-valued successor and source operators; (4) an example is provided, implying that some negative results obtained in the literature may still hold in some typical orthomodular lattice-valued automata.     

Approximation of fuzzy context-free grammars

Fuzzy context-free max- grammar (or FCFG, for short), as a straightforward extension of context-free grammar, has been introduced to express uncertainty, imprecision, and vagueness in natural language fragments. Li recently proposed the approximation of fuzzy finite automata, which may effectively deal with the practical problems of fuzziness, impreciseness and vagueness. In this paper, we further develop the approximation of fuzzy context-free grammars. In particular, we show that a fuzzy context-free grammar under max- compositional inference can be approximated by some fuzzy context-free grammar under max-min compositional inference with any given accuracy. In addition, some related properties of fuzzy context-free grammars and fuzzy languages generated by them are studied. Finally, the sensitivity of fuzzy context-free grammars is also discussed.     

Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.     

An application of stochastic languages to fingerprint pattern recognition

The purpose of this paper is to present a stochastic syntactic approach for representation and classification of fingerprint patterns.The fingerprint impressions are subdivided into sampling squares which are preprocessed for feature extraction. First, a brief summary of application of a class of context-free languages for recognition of fingerprints is presented. Next, using the same set of features, a class of stochastic context-free languages was used to further classify the fingerprint impressions. The recognizers using the class of context-free and stochastic context-free languages are named the first-level and second-level classifiers, respectively. Experimental results in terms of real data fingerprints are presented.