论模糊差分方程xn+1=a+bxn/A+xn-1

讨论非线性模糊差分方程x_n+1=a+bx_n/A+x_n-1（n=0,1,…）正解的存在性、有界性及正解的渐近表现。其中是正模糊数数列、及初始值是正模糊数。     

二阶非线性差分方程的全局吸引性

[摘　要] 研究了差分方程xn+1=a-bxn-1A-xn(a≥0,A≥b≥0)的全局稳定性和正解的周期性.证明了方程的一个正平衡点是一个全局吸引子,并给出了相应的吸引域.     

综合题新编选登

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高阶非线性差分方程的全局吸引性

研究了差分方程 xn 1 =a - bxn- k A - xn( a≥ 0 ,A≥ b≥ 0 )的全局稳定性和正解的周期性质 .证明了方程的一个正平衡点是一个全局吸引子 ,并给出了相应的吸引域     

不定方程x1+x2+…+xn=m的解的个数及在计数问题中的作用

不少计数问题归结为不定方程 x1+ x2+… + xn =m在特定条件下的解的个数问题便迎刃而解 .本文研究不定方程 x1+ x2 +… + xn =m在有关条件下的解的个数问题 ,并举例说明其在计数问题中的应用 .(注 :文中约定 :当 m 相似文献   

Rn上一类拟线性椭圆方程正解的存在性

本文讨论了Rn 上如下一类带临界增长的拟线性椭圆方程正解的存在性 :-div(｜ u｜p- 2 u) -axn｜ u｜p- 2 u xn +｜u｜p- 2u=up - 1 ,xn ≠ 0 ,x∈Rn.这里 ,1

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一阶非线性模糊差分方程动力学行为研究

利用模糊数广义除法,讨论一阶非线性模糊差分方程■,正解的存在性、唯一性以及稳定性,其中(x_n)是正模糊数数列,M,A,B是正模糊数,进一步通过数值例子,以验证结论的有效性。     

关于一道IMO训练题的引申

1998年加拿大IMO训练题 :设x1 ,x2 ,… ,xn+1 是正实数 ,满足条件 11 +x1+ 11 +x2+… +11 +xn+1=1 ,求证 :x1 x2 …xn+1 >nn+1 .上述命题表明 :如果正实数x1 ,x2 ,… ,xn+1满足条件 :11 +x1+ 11 +x2+… + 11 +xn=1 - 11 +xn+1时 ,有xn+1 ≥ nn+1 ni=1xi,所以 :11 +x1+ 11 +x2+…+ 11 +xn≥ 1 - 11 + nn +1 ni=1xi=nn+1 ni=1xi1 + nn +1 ni=1xi=nn+1nn+1 +x1 x2 …xn.由此我们引出一个新命题 :设x1 ,x2 ,… ,xn 是正实数 ,且11 +x1 + 11 +x2 +… + 11 +xn<1 ,　 11 +x1+ 11 +x2+… + 11 +xn≥ nn+1nn+1 +x1 x2 …xn( 1 )事实上 ,由于 11 +x…     

a_(n+1)=p(n)a_n~2+f(n)a_n+r(p(n)≠0)型数列的求解方法

数列问题的背景新颖 ,能力要求高 ,内在联系密切 ,思维方法灵活 ,因此倍受命题者的青睐 .解答数列问题要求熟练掌握数列基础知识 ,灵活运用基本数学思想方法 ,善于转化 .an+1 =p( n) .a2n+ f ( n) .an+ r ( p( n)≠0 )型数列是数列和二次函数、不等式相结合的典范 ,难度较大 .求解此类问题的思维模式是 :观察—归纳—猜想—证明 .求解的主要方法是 :分析法 ,比较法 ,消去法 ,综合法 ,放缩法 ,数学归纳法 .例 1　数列 x1 ,x2 ,… ,由 x1 =12 ,xn+1 =x2n + xn( n =1,2 ,… )给出 ,Sn与 Pn 分别是数列 y1 ,y2 ,y3 ,… ,前 n项的和与积 ,这里 y…     

2012年全国高考大纲卷理科压轴题的解法探究

2012年全国高考大纲卷理科压轴题(第22题)为:题1函数f(x)=x2-2x-3,定义数列{xn}如下:x1=2,xn+1是过两点P(4,5),Qn(xn,f(xn))的直线PQn与x轴交点的横坐标.     

模糊BIK~+-逻辑与非可换模糊逻辑(英文)

为了建立各种可换和非可换模糊逻辑的公共基础(蕴涵片段),提出了一个新的蕴涵逻辑,称为模糊BIK+-逻辑。证明了这一新的蕴涵逻辑的可靠性和弱完备性定理,同时讨论了模糊BIK+-逻辑与各种模糊逻辑之间的关系,以及与它们配套的代数结构之间的关系。     

BIK+-逻辑与非可换模糊逻辑

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Fuzzy addition in the L-fuzzy real line

Under the hypothesis L is a chain, we construct a binary operation ⊕ on the L-fuzzy real line $\text{R}$(L) which reduces to the usual addition on $\text{R}$ if ⊕ is restricted to the embedded image of $\text{R}$ in $\text{R}$(L), which yields a partially ordered, abelian cancellation semigroup with identity, and which is jointly fuzzy continuous on $\text{R}$(L). We show ⊕ is unique, i.e. it is the only extension of addition to $\text{R}$(L) which is consistent. We study the relationship between ⊕ and other fuzzy continuous extensions of the usual addition. We also show that fuzzy translation is a weak fuzzy homeomorphism and, under certain conditions, a fuzzy homeomorphism.     

A type of fuzzy ring

In this study, by the use of Yuan and Lee’s definition of the fuzzy group based on fuzzy binary operation we give a new kind of fuzzy ring. The concept of fuzzy subring, fuzzy ideal and fuzzy ring homomorphism are introduced, and we make a theoretical study their basic properties analogous to those of ordinary rings.      

Editorial

Linear programming problems with fuzzy parameters are formulated by fuzzy functions. The ambiguity considered here is not randomness, but fuzziness which is associated with the lack of a sharp transition from membership to nonmembership. Parameters on constraint and objective functions are given by fuzzy numbers. In this paper, our object is the formulation of a fuzzy linear programming problem to obtain a reasonable solution under consideration of the ambiguity of parameters. This fuzzy linear programming problem with fuzzy numbers can be regarded as a model of decision problems where human estimation is influential.     

群,环上的Fuzzy关系

一些学者已对群和环上的Ｆｕｚｚｙ关系进行了研究，本文进一步研究了群、环上的Ｆｕｚｚｙ关系，得出了若干重要的结论。     

Toward extended fuzzy logic—A first step

Fuzzy logic adds to bivalent logic an important capability—a capability to reason precisely with imperfect information. Imperfect information is information which in one or more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true. In fuzzy logic, results of reasoning are expected to be provably valid, or p-valid for short. Extended fuzzy logic adds an equally important capability—a capability to reason imprecisely with imperfect information. This capability comes into play when precise reasoning is infeasible, excessively costly or unneeded. In extended fuzzy logic, p-validity of results is desirable but not required. What is admissible is a mode of reasoning which is fuzzily valid, or f-valid for short. Actually, much of everyday human reasoning is f-valid reasoning.f-Valid reasoning falls within the province of what may be called unprecisiated fuzzy logic, FLu. FLu is the logic which underlies what is referred to as f-geometry. In f-geometry, geometric figures are drawn by hand with a spray pen—a miniaturized spray can. In Euclidean geometry, a crisp concept, C, corresponds to a fuzzy concept, f-C, in f-geometry. f-C is referred to as an f-transform of C, with C serving as the prototype of f-C. f-C may be interpreted as the result of execution of the instructions: Draw C by hand with a spray pen. Thus, in f-geometry we have f-points, f-lines, f-triangles, f-circles, etc. In addition, we have f-transforms of higher-level concepts: f-parallel, f-similar, f-axiom, f-definition, f-theorem, etc. In f-geometry, p-valid reasoning does not apply. Basically, f-geometry may be viewed as an f-transform of Euclidean geometry.What is important to note is that f-valid reasoning based on a realistic model may be more useful than p-valid reasoning based on an unrealistic model.     

A new approach to fuzzy programming

A fuzzy program is defined in the usual way as a sequence of statements (instruction) which are considered as functions (possibly fuzzy functions) and fuzzy predicates defined on the given input domain. The essential difference in the approach presented in this paper is the new interpretation of the execution of fuzzy programs, and a new method of evaluating fuzzy predicates. The result of the fuzzy program execution is an appropriate fuzzy subset in the output domain.