一种集成遗传算法与模糊推理的粗糙集数据分析算法

粗糙集数据分析的主要优点在于它不要求任何关于被处理数据的先验或额外的知识,文章利用其对数据库进行分析计算,自动获取数据库在各个层次上的规则集。在保证量化后的数据库具有最大一致性的前提下,利用遗传算法求取连续属性值的最优量化区间个数及各个区间分点值。同时将量化区间进行模糊化,将清晰规则集转化为模糊规则集,利用模糊推理进行决策以提高鲁棒性。通过对UCI中几个数据库的测试验证了所提出算法的有效性。     

Rough sets and Boolean reasoning

In this article, we discuss methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis.     

A simple O(n log n) algorithm for finding the maximum distance between two finite planar sets

A simple O(n log n) algorithm is presented for computing the maximum Euclidean distance between two finite planar sets of n points. When the n points form the vertices of simple polygons this complexity reduces to O(n).     

Optimal algorithms for computing the minimum distance between two finite planar sets

It is shown in this paper that the minimum distance between two finite planar sets of n points can be computer in O(n log n) worst-case running time and that this is optimal to within a constant factor. Furthermore, when the sets form a convex polygon this complexity can be reduced O(n).     

Rough Self Organizing Map

A rough self-organizing map (RSOM) with fuzzy discretization of feature space is described here. Discernibility reducts obtained using rough set theory are used to extract domain knowledge in an unsupervised framework. Reducts are then used to determine the initial weights of the network, which are further refined using competitive learning. Superiority of this network in terms of quality of clusters, learning time and representation of data is demonstrated quantitatively through experiments over the conventional SOM.     

基于直觉模糊推理的目标识别方法

针对目标识别特征值的不确定性问题,提出一种基于直觉模糊推理的目标识别方法．首先,分析现有目标识别方法的不确定性与局限性,并对空中目标识别问题及目标特征进行描述;然后,设计系统状态属性变量的隶属度函数与非隶属度函数;最后,建立直觉模糊推理规则,设计推理合成算法和解模糊算法,并检验所建规则的合理性．仿真实例验证了所提方法的有效性与适用性．     

Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning

Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users.The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem.We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.     

粗糙集与模糊系统集成的化学模式分类方法及其应用

模糊方法是一种有效的化学模式分类方法,但模糊规则的获取和相关参数的确定较为困难。对此,本文采用粗糙集方法,无需任何先验知识,约简系统,获取最简规则集,在此基础上构建结构合理．适用于分类的模糊-神经网络系统,并根据规则的统计性质和离散化结果初始化网络参数,采用LM方法训练网络;在橄榄油模式分类建模的应用中,该方法训练收敛速度快,所建模型预测性能良好,要优于现代统计方法和前馈神经网络。