模糊数在股本权证定价中的应用

采用Ukhov权证定价模型求解权证价值的过程中,需要求解一个非线性方程组．但是采用数值法得到的最优解与精确解往往有一定偏差．针对这个情况,本文采用模糊数刻画非线性方程组的解,得到不确定形式的股本权证定价模型,并给出一定可信度下权证的模糊价格区间．同时也给出了给定任意一个权证价格求其对应的可信度的优化算法．数值算例验证了该文方法的有效性．     

用改进的人工萤火虫群优化算法求解非线性方程组

针对传统算法复合形法在求解非线性方程组时依赖于初始值的选定和人工萤火虫群算法(GSO)算法在求解非线性方程组时求解精度低的缺点,提出一种基于复合形法的GSO算法(CGSO)求解非线性方程组方法.改进后的算法克服了传统算法的缺点且有效的提高了GSO算法在求解非线性方程组的精度.最后,通过对6个非线性方程组的仿真实验结果和传统算法,以及其他群智能算法进行比较,进而说明了CGSO算法的有效性.     

三个方程耦合的Schrödinger方程组的基态

研究由三个方程耦合的非线性Schr?dinger方程组,它们源于非线性光学和Bose-Einstein凝聚.考虑了两种类型:含有周期位势的方程组和含有势阱位势的方程组.借助于广义的Nehari流形以及精细的能量估计,证明了当相互作用位势适当小时,这两类非线性Schr?dinger方程组存在正的基态.     

一类交叉耦合非线性反应扩散方程组的爆破分析

主要研究了具有混合型的多重非线性项的抛物方程组的初边值问题.方程组中的非线性项是幂函数和指数混合型的.这些非线性项组合出了源-流交叉耦合,通过比较原理得到了方程组的上下解,并得到了解有限时刻爆破的临界指标.     

阻尼Gauss-Newton方法解非线性不等式组

本文研究了非线性不等式组的求解问题.利用了阻尼Gauss-Newton方法求解非线性方程组,获得了该算法的全局收敛性,推广了Gauss-Newton法在解非线性方程组方面的应用.     

三个方程耦合的Schr?dinger方程组的基态（英文）

研究由三个方程耦合的非线性Schr?dinger方程组,它们源于非线性光学和Bose-Einstein凝聚.考虑了两种类型:含有周期位势的方程组和含有势阱位势的方程组.借助于广义的Nehari流形以及精细的能量估计,证明了当相互作用位势适当小时,这两类非线性Schr?dinger方程组存在正的基态.     

电力系统中地网腐蚀诊断的一种新的数学模型及其仿真计算

对电力系统中具有重大应用价值的地网腐蚀诊断问题抽象出仿真求解的一种新的数学模型:即求解带约束的非线性隐式方程组模型.但由于问题本身的物理特性决定了所建立的数学模型具有以下特点:一是非线性方程组为欠定方程组,而且非线性程度非常高;二是方程组的所有函数均为隐函数;三是方程组附加若干箱约束条件.这种特性给模型分析与算法设计带来巨大困难.对于欠定方程组的求解,文中根据工程实际背景,尽可能地扩充方程的个数,使之成为超定方程组,然后对欠定方程组和超定方程组分别求解并进行比较.将带约束的非线性隐函数方程组求解问题,转化为无约束非线性最小二乘问题,并采用矩阵求导等技术和各种算法设计技巧克服隐函数的计算困难,最后使用拟牛顿信赖域方法进行计算.大量的计算实例表明,文中所提出的数学模型及求解方法是可行的.与目前广泛采用的工程简化模型相比较,在模型和算法上具有很大优势.     

一类非线性耗散方程组的不变子空间及其精确解

利用不变子空间方法研究一类在孤立子理论中具有广泛应用的非线性耗散方程组,确定出非线性耗散方程组在它所容许的不变子空间W_3~1×W_2~2中的完全分类,构造了方程组的精确解或者将方程组约化为有限维动力系统.文中的结果进一步推广了不变子空间理论在非线性偏微分方程中的应用.     

含指数增长非线性项的Chern-Simons-Schrdinger方程组解的存在性（英文）

本文研究了带指数增长的非线性项的非线性Chern-Simons-Schrdinger方程组.利用山路引理的方法,得到该方程组解的存在性.     

一类非线性方程组的求解

对于一个给定的非线性方程组,通过一系列的变化,可以将其构造成一个函数,从而把非线性方程组的求解问题转换为求函数极小值问题.通过利用正交表的数据分析方法,给出了求函数极小值进而求解非线性方程组的方法,这种方法得到的解比已有的更精确,且大大缩减了复杂方程组的计算量,用时少,不需要初始值.最后,采用Matlab软件,验证了其可行性和有效性.     

A note on chance constrained programming with fuzzy coefficients

This paper deals with nonlinear chance constrained programming as well as multiobjective case and goal programming with fuzzy coefficients occurring in not only constraints but also objectives. We also present a fuzzy simulation technique for handling fuzzy objective constraints and fuzzy goal constraints. Finally, a fuzzy simulation based genetic algorithm is employed to solve a numerical example.     

Monomial geometric programming with fuzzy relation equation constraints

Monomials are widely used. They are basic structural units of geometric programming. In the process of optimization, many objective functions can be denoted by monomials. We can often see them in resource allocation and structure optimization and technology management, etc. Fuzzy relation equations are important elements of fuzzy mathematics, and they have recently been widely applied in fuzzy comprehensive evaluation and cybernetics. In view of the importance of monomial functions and fuzzy relation equations, we present a fuzzy relation geometric programming model with a monomial objective function subject to the fuzzy relation equation constraints, and develop an algorithm to find an optimal solution based on the structure of the solution set of fuzzy relation equations. Two numerical examples are given to verify the developed algorithm. Our numerical results show that the algorithm is feasible and effective.     

修正的模糊最小一乘线性回归

对文献[1]提出的基于对称三角模糊数的模糊最小一乘线性回归进行修正和扩展,给出模糊最小一乘线性回归模型的三种不同形式,并将其转化为线性规划或非线性规划问题进行求解。最后,给出几个数值实例,通过计算和比较,结果表明三种模糊最小一乘线性回归模型都具有非常好的拟合性。     

基于多目标规划法的模糊线性回归分析

讨论输入、输出均为模糊数,回归系数为实数时的模糊线性回归分析。由于模糊最小二乘线性回归容易受异常值的影响,而最小一乘法能有效地降低回归模型的误差。为此,基于最小一乘法,建立多目标规划模型并将其转化为非线性规划问题进行求解,从而实现模糊线性回归模型的参数估计。最后,结合一个数值实例,验证和比较该方法的合理性和优越性。     

模糊系数规划

给出了一模糊系统规划的定义,该定义与通常的模糊规划义有所不同,它容许规划中的目标函数系数和所有约束函数系数可以是模糊数,并且容许既有模糊系数不等式的束函数又有模糊和所有约束函数系数都可以提模糊数,并且容许既有模糊系数不等式的束函数又有模糊系数等式约束函数,本文还对满足一定条件的模糊系数规划,包括模糊系数线性规划和模糊系数二次规划,给出了切实可行的求解方法。     

Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.     

Stability of multiobjective NLP problems with fuzzy parameters in the objectives and constraints functions

This paper deals with the stability of multiobjective nonlinear programming problems with fuzzy parameters in the objectives and constraints functions. These fuzzy parameters are characterized by fuzzy numbers. The existing results concerning the qualitative analysis of the notions (solvability set, stability sets of the first kind and of the second kind) in parametric nonlinear programming problems are reformulated to study the stability of multiobjective nonlinear programming problems under the concept of α-pareto optimality. An algorithm for obtaining any subset of the parametric space which has the same corresponding α-pareto optimal solution is also presented. An illustrative example is given to clarify the obtained results.     

Fuzzy Bayesian system reliability assessment based on exponential distribution

The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator of system reliability based on Exponential distribution by invoking the well-known theorem called “Resolution Identity” in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g., GAMS or LINGO (LINDO).     

A fuzzy shortest path with the highest reliability

This paper concentrates on a shortest path problem on a network where arc lengths (costs) are not deterministic numbers, but imprecise ones. Here, costs of the shortest path problem are fuzzy intervals with increasing membership functions, whereas the membership function of the total cost of the shortest path is a fuzzy interval with a decreasing linear membership function. By the max–min criterion suggested in [R.E. Bellman, L.A. Zade, Decision-making in a fuzzy environment, Management Science 17B (1970) 141–164], the fuzzy shortest path problem can be treated as a mixed integer nonlinear programming problem. We show that this problem can be simplified into a bi-level programming problem that is very solvable. Here, we propose an efficient algorithm, based on the parametric shortest path problem for solving the bi-level programming problem. An illustrative example is given to demonstrate our proposed algorithm.     

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.