脂肪腈结构与凝聚型性能之间定量关系的研究

借助于分子拓扑学探讨了脂肪腈的凝聚型性能与分子结构之间的关系，提出一个结构基础明确的定量关系式．对乙腈到二十一烷腈的计算结果表明，沸点、密度和折光指数的计算值都很接近实验值．应用这一定量关系，不仅能够合理表征脂肪睛结构与凝聚型性能的关系，而且有助于揭示物质结构与性能关系之间的奥秘。     

一些Fuzzy映象的重合度

本文讨论一些Fuzzy映象的重合度的存在性,得出了一些新的重合度定理.本文所得结果统一和发展了引文[5～9]中的许多重要结果.     

干部功能的模糊测评

利用模糊数学的分析方法对干部功能进行综合测评,确定干部的功能等级。     

A New Integrated Design Method Based on Fuzzy Matter-Element Optimization

This paper puts forward a new integrated design met ho d based on fuzzy matter-element optimization.On the based of analyzing the mod el of multi-objective fuzzy matter-element , the paper defines the m atter-element weightily and changes solving multi-objective fuzzy optimization into solving dependent function K(x) of the single-objective optimization according to the optimization criterion. The paper particularly describes the realization approach of GA process of multi -objective fuzzy matter-eleme...     

模糊集重心法在评估科研院所绩效素质中的应用

为了评价中国科研院所的绩效和素质提出了一个由人才资源、成果绩效、发展后劲和财务管理指标组成的评价指标体系 ,使用模糊集重心法解决了各种线性和非线性问题。由于此模型具有中间过程的分析能力 ,大大丰富了提供给决策部门的信息。这种方法可成为研究我国中央级科研院所的等级分布、类型优势分析、改革现状估计、潜力比较和指标灵敏度分析的有力工具     

产量──密度模型讨论

本文给出产量与密度函数关系     

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.     

现代区域地理学初论

现代区域地理学与传统区别在于加强应用性。它强调人与自然环境相互关系，承认区域性质或功能发现的认识过程，提倡理论与方法的现代化与多样化。     

上海郊区居民和台湾高山族的体质比较

收集了上海郊区13个具有明显方言差异人群的头面部数据4项，和台湾高山族10个群体的参数进行聚类分析，发现上海郊区居民中的南方土著人群与台湾高山的遗传关系较近。     

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.