Ranking Functions Induced by Weighted Average of Fuzzy Numbers

In this paper we present two definitions of possibilistic weighted average of fuzzy numbers, and by them we introduce two different rankings on the set of real fuzzy numbers. The two methods are dependent on several parameters. In the first case, the parameter is constant and the results generalize what Carlsson and Fuller have obtained in (2001). In the second case, the parameter is a function, not fixed a priori by the decision maker, but it depends on the position of the interval on the real axe. In all the two cases we call the parameter degree of risk, which takes into account of a risk-tendency or aversion of the decision maker.     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

基于模糊方法的多人合作对策的研究

多人合作对策模型中联盟的收入和总体的收入常常出现相互矛盾的情况 ,此时核是空集 .由于不存在核 ,无法用 Nash-Harsanyi谈判模型求解 .采用模糊数学方法 ,调整模型中线性约束的右端系数 ,使核在一定程度上是非空集合 ,得到模糊意义下的 Nash平衡解 .该方法一定程度上解决了各联盟收入与总体收入的矛盾 .最后通过一个算例说明该方法的可行性 .     

Clustering Effect on the Statistical Estimation Accuracy of Distribution Density

The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analyzed by means of the Monte Carlo method. It is shown that the soft clustering is more advantageous than the hard one. While a moderate increase in the number of clusters also increases the calculation time, it considerably reduces the estimation error.     

一个新的最优模糊存储模型

提出一个新的具有积压定单的关于模糊订购量的模糊存储模型.在模糊函数原理下,给出了模糊总存储成本.为了寻找最优解,把最优模糊存储模型转化为双目标最优化模型,利用L ingo8.0求解不等式约束问题,我们发现最优解都是确定的实数.此外,当模糊订购量和模糊总需求都是三角形(或权重均为1/2梯形)模糊数时,我们提出模型的最优解与经典的具有积压定单存储模型具有相同的结果.     

Fuzzy HX环的推广

为改进Fuzzy HX环的结果,使之包含Fuzzy商环,提出了弱Fuzzy HX环的概念,研究了它的性质与结构,并重新讨论了拟Fuzzy商环,证明了在正则条件下拟Fuzzy商环与弱Fuzzy HX环的统一性：同时也得到了一致弱Fuzzy HX环与普通Fuzzy商环的关系。     

A theorem on sequentiality and compactness, with its fuzzy extension

An amended proof of a theorem of Franklin's on sequentiality and sequential compactness is presented, and a corresponding fuzzy version formulated using the theory of quasi-coincidence.     

模糊集单调型分解定理

模糊集的分解集定理有两种形式，第一种形式与模糊集的λ—截集有关：Ａ＝其中Ａ＿λ是的λ—截集￣［１］．第二种形式与集合套有关：其中Ｈ（λ）为集合套￣［２］或者Ａ＝其中Ｈ（λ）为集合套，Ｑ为（０，１）的可列稠密子集＿［３］，．其实，这两种形式在本文提出的基本模糊集的概念下，可以统一起来成为任何模糊集可以分解为一些（可数或不可数）基本模糊集的和。不仅如此，本文还提出简单模糊集的概念，并证明了任何模糊集都可以表示成为可数个单调上升的简单模糊集的和，或等价地，可以表示成可数个单调上升的简单模糊集的极限，所用的证明方法是构造性的，所以对模糊集的结构也得出了一个清晰的认识．     

动态模糊随机信息处理的数学方法

本文系统地概述了我们近年来提出的动态模糊随机信息处理的数学方法,内容包括模糊随机变量、模糊随机过程和模糊随机微分方程的基本解法等方面的基本概念、基本定义和某些重要的定理,以及动态系统的模糊随机响应与可靠性分析的方法等。这些方法是为我们研究工程实际问题的需要逐步发展起来的,对于处理某些类型的问题简便实用。     

Fuzzy集的基数

关于Fuzzy集的基数，[1]中曾对有限支集的Fuzzy集以及极苛刻条件下的无限Fuzzy集给出过一种定义。但是，正如本文将要指出的那样，该定义是不合理的。本文将从研究Fuzzy映射入手，给出Fuzzy集基数的一般性定义。基于这一定义，不但得到有关基数的大部分结论，而且有其自身的特殊性。