基于模糊推理的人工气候室温度PID控制器参数的整定

具有较大容积的人工气候室温度控制是一个典型的大时滞、非线性复杂控制问题,应用基于模型的控制理论来进行分析和综合非常困难。运用模糊数学的基本理论和方法,提出了一种基于模糊推理的PID控制器参数自动整定方法。该方法将难以定量描述的参数调整经验用模糊集合来表示,并把对应的模糊推理规则作为知识存入控制器中,控制器根据控制系统的实际运行情况,在实现对PID参数的自动调整。实验表明,提出的模糊PID控制器参数整定方法,适合于具有较大可控率的人工气候室温度控制系统设计,在同类控制系统设计中具有先进性。     

浅谈粗糙集理论及其应用进展

粗糙集理论是一种新型的处理模糊和不确定知识的数学工具。目前已在人工智能、知识与数据发现、模式识别与分类、故障检测等方面得到了广泛应用。首先描述了粗糙集的基本算法及其复杂度，包括等价关系，上下近似及约简算法；然后对粗糙集在一些领域中的应用进展情况进行了论述，例如模式识别与人工神经元网络等，最后给出了建议的研究方向。     

基于案例推理的边坡稳定性评价系统及应用

探讨了基于案例推理技术在边坡稳定性评价中的应用，提出了边坡案例的表示、聚类索引、检索和调整模型．并运用Access建立了边坡案例库，设计并开发了基于案例推理的边坡稳定性评价系统，实现了遗传算法优化属性权重和欧式距离、类比分析及神经网络的3种检索方法，最后运用边坡工程实例对系统进行了验证。结果表明，该系统是合理有效的，对工程边坡稳定性评价将具有重要的理论意义和工程实用价值。     

There is more to aptitude than cognitive capacities: Comment.

Comments on the original article "Sex Differences in Intrinsic Aptitude for Mathematics and Science?: A Critical Review," by E. S. Spelke (see record 2005-15840-001). Spelke's critical review is a research-based rebuttal (though implicitly) of Summers's (2005) speech that posited a hypothesis that one of the reasons why women are underrepresented in math, science, and engineering may be sex differences in intrinsic aptitude for mathematics and science. Putting aside the question of whether the empirical evidence was sufficiently reviewed, the way Spelke conceptualized aptitude as a static rather than a dynamic quality (namely, cognitive capacities) rendered her critique of the "differences in intrinsic aptitude" hypothesis less effective in many respects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach     

Computational methods for database repair by signed formulae*

We introduce a simple and practical method for repairing inconsistent databases. Given a possibly inconsistent database, the idea is to properly represent the underlying problem, i.e., to describe the possible ways of restoring its consistency. We do so by what we call signed formulae, and show how the ‘signed theory’ that is obtained can be used by a variety of off-the-shelf computational models in order to compute the corresponding solutions, i.e., consistent repairs of the database. *This paper is a revised and extended version of [9].     

Office自动化编程的设计及应用

自动化是一种技术,利用该技术可以将Office的一些功能合并到我们的应用程序中,可以在应用程序中不让用户看到Mi-crosoftWord。而Office软件的强大的编辑、排版和表格功能是很多应用所需的。本文正是将Office自动化编程技术和我们的数学资源库系统结合,将资源转换成Word文档提供给用户,具有良好的应用效果。     

约束概念格及其构造方法

概念格是一种有效的数据分析和知识提取的形式化工具．然而，随着要处理的数据量的剧增，基于原始形式背景构造出的概念格结点数目庞大，占用大的存储空间，同时概念格结点中一些属性集形成的内涵，用户并不都感兴趣，因而从中提取用户需求知识费时．为了降低概念格构造的时空复杂性，增强实用性和针对性，首先采用谓词逻辑描述用户感兴趣的背景知识，并将背景知识引入到概念格结构中，提出了一种新的概念格：约束概念格．在此基础上，提出了基于背景知识的约束概念格构造算法CCLA．理论分析表明，该算法能有效地减少概念格的存储空间和建格时间．最后，采用恒星天体光谱数据作为形式背景，实验验证了该算法的有效性．     

A preferential semantics for causal reasoning about action

One of the principal concerns in the research area of Reasoning about Action is determining the ramifications of actions in changing environments. A particular tendency emerging in recent literature endorses the explicit incorporation of causal knowledge in logic-based action theories. It is argued that causal extensions not only enhance the expressive power of theories of action, but may also provide more concise and intuitive representations. This paper investigates semantics for causal reasoning about action and change. It does so by exploring the role of several fundamental underlying principles, such as the Principle of Minimal Change and the Principle of Causal Change. This work culminates in a general unifying semantics for a class of action theories represented by a number of recent and influential approaches – in particular, the causal relationship approach of Thielscher and the causal systems with fixed-points suggested by McCain and Turner. The unifying augmented preferential semantics, emerging as a result of this study, captures both Principles of Change and shows their clear and distinct roles.     

Possibilistic uncertainty handling for answer set programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.