建立在模糊逻辑上的模糊元胞自动机

总结了经典元胞自动机模型理论,并在此基础上把模糊逻辑引入元胞自动机模型中.通过对模糊元胞自动机的基本原理的分析,定义了模糊元胞自动机模型.模糊元胞自动机模型可以处理模糊信息,并且使模拟与现实世界的情况更为接近.     

模糊自动机的强连通性及群自动机

为了更好地研究模糊自动机的结构和性质,采用代数的方法,在传统的模糊有限状态自动机的基础上,通过定义状态集合为代数群的自动机,讨论了这一类自动机的连通性和正则性,这丰富了模糊自动机理论．     

可逆模糊自动机

首先提出了可逆模糊自动机的概念,研究了能被可逆模糊自动机接受的语言(简记为F(∑))的一些性质.其次给出了自由群上被可逆模糊自动机接受的模糊子集的概念,详细研究了可逆模糊语言与经典可逆语言的关系.最后,通过引入语法幺半群刻画了F(∑)的代数性质.通过这些性质可以有效的判断一个模糊语言是否能被一个可逆模糊自动机接受.     

通用模糊自动机

首先使用模糊语言的分解定义了通用模糊自动机:这个自动机以该模糊语言的分解作为状态,通过模糊语言的包含度定义其转移函数。其次定义了模糊自动机上的态射,证明了接受给定模糊语言的所有自动机都可以标准地映射到该语言对应的通用模糊自动机上,这个性质称为通用性。最后进一步讨论了模糊商自动机,模糊m-最小自动机,模糊最小自动机及与通用模糊自动机之间的关系。     

两类循环模糊自动机的弱等价性

定义循环模糊自动机和循环模糊有限状态自动机,并讨论了这两类循环模糊自动机的弱等价性.     

循环模糊有限自动机的同态性质

讨论了模糊有限自动机(即模糊Mealy机)的同态性质和循环模糊有限自动机的同态性质,证明了每个模糊有限自动机都是有限个循环模糊有限自动机的直和的同态象。     

模糊有限自动机的kronecker积

在矩阵理论框架下,引入了模糊有限自动机转移矩阵,变换矩阵半群以及覆盖概念.定义了模糊有限自动机Kronecker积,讨论了其转移矩阵性质及变换矩阵半群间的覆盖关系.     

模糊剩余自动机

在[0,1]格值区间上引入了模糊剩余自动机(FRFA)的概念:若一个模糊自动机(FFA)的每一个状态都定义了其接受语言的一个剩余语言,则称为模糊剩余自动机.讨论了模糊剩余自动机的一些性质以及模糊自动机的消去与饱和运算.在定义既约模糊剩余语言的基础上定义了标准模糊剩余自动机,并给出了构造方法.证明了一个模糊正则语言对应的标准FRFA即为识别这个语言的最小状态FFA,此研究为模糊自动机的状态最小化研究提供了另一种研究思路.     

模糊无限状态自动机及其收敛性

到目前为止,我们所研究的模糊或非模糊的自动机都是有限状态自动机.然而,关于无限状态自动机的定义及它的稳定性和收敛性都没有被讨论过.本文中,我们使用离散的反馈神经网络及网络输出空间划分方法,同时,在梯度更新算法中使用伪梯度方法,给出了模糊无限状态自动机收敛到模糊有限状态自动机的证明.     

最小化基于合成模糊变换的模糊自动机

经过重新定义模糊自动机,使得模糊自动机的识别过程与一个合成模糊变换(CF变换)一致,而且得到了尊重合成模糊变换的最粗分类即为状态集的最粗等价分类这一重要结论.在对尊重合成模糊变换的最粗分类的讨论中,给出了找到尊重合成模糊变换的最粗分类的有限步算法,亦即状态集的最粗等价分类和最小化模糊自动机的算法.该算法不仅给出了最长运算时间,而且还给出可终止算法的条件,使得运算更为可行和简便.     

Fuzzy automata and life

Boolean cellular automata may be generalized to fuzzy automata in a consistent manner. Several fuzzy logics are used to create 1‐dimensional automata and also 2‐dimensional automata that generalize the game of life. These generalized automata are investigated and compared to their Boolean counterparts empirically and using rule entropy and repeated input response functions. Fuzzy automata offer new mechanisms for classification of classical automata and can be used for insight into their qualitative behavior. © 2002 Wiley Periodicals, Inc.     

具有模糊支付的Moran过程演化博弈动态

以往对演化博弈的研究都假设个体从博弈中获得的支付是确定的并以精确的数来表示。然而由于受环境中各种不确定因素的影响,个体博弈时所获得的支付并不是一个精确的数值,而需要用一个模糊数来表示。本文研究模糊支付下2×2的对称博弈, 利用模糊数的运算, 分析具有模糊支付的有限种群Moran过程演化动态。在弱选择下以梯形模糊数和三角模糊数表示博弈支付,计算策略的模糊扎根概率,分析自然选择有利于策略扎根及策略成为模糊演化稳定策略的条件。将经典博弈推广到模糊环境中丰富了演化博弈理论,更具有现实意义。     

凸合成模糊对策的模糊稳定集

本建立了凸合成模糊对策的模型，并得到了凸合成模糊对策的模糊稳定集，可由子对策的模糊稳定集表达出来。从而解决了凸合成模糊对策的解的结构问题。     

可转移效用下的动态模糊联盟的广义核心解

利用模糊数学相关理论,对具有可转移效用的模糊合作对策进行了研究.采用调整相应系数的方法给出了此类对策的广义核心解和广义稳定集的概念,讨论了它们之间的关系.提出一种新的分配方案—不将联盟总收益分配给所有局中人,而保留一部分从而实现收益的再分配.该成果解决了对策传统解在现实应用中的局限性,特别是对现实生活中的资源再分配问题具有一定的参考和应用价值.     

An Approach to Fuzzy Noncooperative Nash Games

Systems that involve more than one decision maker are often optimized using the theory of games. In the traditional game theory, it is assumed that each player has a well-defined quantitative utility function over a set of the player decision space. Each player attempts to maximize/minimize his/her own expected utility and each is assumed to know the extensive game in full. At present, it cannot be claimed that the first assumption has been shown to be true in a wide variety of situations involving complex problems in economics, engineering, social and political sciences due to the difficulty inherent in defining an adequate utility function for each player in these types of problems. On the other hand, in many of such complex problems, each player has a heuristic knowledge of the desires of the other players and a heuristic knowledge of the control choices that they will make in order to meet their ends.In this paper, we utilize fuzzy set theory in order to incorporate the players' heuristic knowledge of decision making into the framework of conventional game theory or ordinal game theory. We define a new approach to N-person static fuzzy noncooperative games and develop a solution concept such as Nash for these types of games. We show that this general formulation of fuzzy noncooperative games can be applied to solve multidecision-making problems where no objective function is specified. The computational procedure is illustrated via application to a multiagent optimization problem dealing with the design and operation of future military operations.     

基于三角模糊数的凸合成模糊对策解的结构

将凸合成模糊对策的特征函数用三角模糊数的形式表示出来,并以三角模糊数表示局中人的参与度,从而建立了一个新的凸合成模糊合作对策的模型.在此模型的基础上,给出了凸合成模糊对策的三角核心和三角稳定集,并证明了上述解可由子对策的核心和稳定集表达出来.     

Fuzzy local ω-systems

The natural language computing today demands for the study of ω-languages. Therefore in this respect it is convenient to consider fuzzy ω-languages. In this paper, the concept of fuzzy local ω-language, Büchi fuzzy local ω-language, and some closure properties of fuzzy local ω-languages are presented. We introduce deterministic fuzzy finite automaton with different acceptance mode on fuzzy ω-languages and establish the relationship between these various classes of fuzzy ω-languages. We have defined deterministic fuzzy local automaton and also establish relationships between deterministic fuzzy local automaton, fuzzy local ω-language and Büchi fuzzy local ω-language. Further we show that every fuzzy regular ω-language is a projection of a Büchi fuzzy local ω-language.     

Bisimulations for fuzzy automata

Bisimulations have been widely used in many areas of computer science to model equivalence between various systems, and to reduce the number of states of these systems, whereas uniform fuzzy relations have recently been introduced as a means to model the fuzzy equivalence between elements of two possible different sets. Here we use the conjunction of these two concepts as a powerful tool in the study of equivalence between fuzzy automata. We prove that a uniform fuzzy relation between fuzzy automata A and B is a forward bisimulation if and only if its kernel and co-kernel are forward bisimulation fuzzy equivalence relations on A and B and there is a special isomorphism between factor fuzzy automata with respect to these fuzzy equivalence relations. As a consequence we get that fuzzy automata A and B are UFB-equivalent, i.e., there is a uniform forward bisimulation between them, if and only if there is a special isomorphism between the factor fuzzy automata of A and B with respect to their greatest forward bisimulation fuzzy equivalence relations. This result reduces the problem of testing UFB-equivalence to the problem of testing isomorphism of fuzzy automata, which is closely related to the well-known graph isomorphism problem. We prove some similar results for backward-forward bisimulations, and we point to fundamental differences. Because of the duality with the studied concepts, backward and forward-backward bisimulations are not considered separately. Finally, we give a comprehensive overview of various concepts on deterministic, nondeterministic, fuzzy, and weighted automata, which are related to bisimulations.