改进的变精度粗糙集在概念格构造中的应用研究

根据可变精度粗糙集的R-上、下分布约简算法的优势,结合概念格形式背景的特点,将二者有机地结合,提出了基于变精度粗糙集的概念格约减算法。分析了变精度粗糙集模型中的R值的选取算法、可辨识矩阵属性约简,以及传统算法中存在的问题,并进行了改进。最后,为了验证改进后算法的有效性,设计了基于变精度粗糙集的概念格生成系统,通过一个实例演示了构造概念格的整个过程,并通过实验证明了算法的有效性。     

求解最小闭包球问题改进的SMO-型算法

研究n维空间中m个点的最小闭包球(MEB)问题。通过结合确定并删除内部点的技术到序列最小最优化(SMO)方法中,提出一种近似求解MEB问题的改进的SMO-型算法。证明了该算法具有线性收敛性。数值结果表明对于一些mn的大规模数据集,改进的算法与原算法相比速度可以提高10倍以上。尤其,当n等于100且m等于100000时,改进的SMO-型算法仅需执行8s。此外,对于n等于10000且m等于1000的大规模数据集,改进的算法也仅需执行150s。     

基于RSKNN分类改进算法

RSKNN算法是K近邻算法的一种改进算法,该算法基于变精度粗糙集理论,能在保证一定分类精度的前提下,有效地降低分类样本的计算量,并且提高计算效率和分类精度．由于RSKNN算法对属性的依赖度较高,在分类时容易受到伪近邻的影响,导致RSKNN算法的分类精度受到一定程度的影响．针对存在问题,本文提出一种新颖的基于RSKNN算法的改进算法SMwRSKNN,该算法在RSKNN算法的基础上引入类别子空间的思想,以降低冗余属性和伪近邻对分类的影响．在UCI公共数据集上的实验结果表明,SMwRSKNN算法比RSKNN算法具有更高的分类精度．     

不确定性数据挖掘在IDS中的应用

简要介绍了入侵检测系统,研究了IDS中入侵检测模型和不确定性数据挖掘方法及过程,对不确定性数据挖掘算法进行了分析评价,并使用KDD CUP 1999数据集在Matlab中进行了实证分析处理及测试。     

气象信息网络安全风险评估

针对气象信息网络事故的随机性,复杂性和不确定的特点,以及在气象信息网络安全风险评估选取评估指标过程中存在的主观性,通过采用粗糙集改进的属性约简算法对用于评估的指标集进行约简,从而剔除其中的对最终结果不造成影响的指标,并且利用相对约简建立用于气象信息网络安全风险评估的层次指标体系,应用模糊层次分析法对选定的气象信息网络进行安全风险评估,比单纯的依靠专家打分法更具有科学依据和数据支持。     

Light scattering from anisotropic, randomly rough, perfectly conducting surfaces

An approach is introduced for performing rigorous numerical simulations of electromagnetic wave scattering from randomly rough, perfectly conducting surfaces. It is based on a surface integral technique, and consists of determining the unknown electric surface current densities from which the electromagnetic field everywhere can be determined. The method is used to study the scattering of a p-polarized beam from an anisotropic Gaussian, randomly rough, perfectly conducting surface. It is demonstrated that the surface anisotropy gives rise to interesting and pronounced signatures in the angular intensity distribution of the scattered light. The origins of these features are discussed.     

Soft sets and soft rough sets

In this study, we establish an interesting connection between two mathematical approaches to vagueness: rough sets and soft sets. Soft set theory is utilized, for the first time, to generalize Pawlak’s rough set model. Based on the novel granulation structures called soft approximation spaces, soft rough approximations and soft rough sets are introduced. Basic properties of soft rough approximations are presented and supported by some illustrative examples. We also define new types of soft sets such as full soft sets, intersection complete soft sets and partition soft sets. The notion of soft rough equal relations is proposed and related properties are examined. We also show that Pawlak’s rough set model can be viewed as a special case of the soft rough sets, and these two notions will coincide provided that the underlying soft set in the soft approximation space is a partition soft set. Moreover, an example containing a comparative analysis between rough sets and soft rough sets is given.     

Positive approximation and converse approximation in interval-valued fuzzy rough sets

Methods of fuzzy rule extraction based on rough set theory are rarely reported in incomplete interval-valued fuzzy information systems. Thus, this paper deals with such systems. Instead of obtaining rules by attribute reduction, which may have a negative effect on inducting good rules, the objective of this paper is to extract rules without computing attribute reducts. The data completeness of missing attribute values is first presented. Positive and converse approximations in interval-valued fuzzy rough sets are then defined, and their important properties are discussed. Two algorithms based on positive and converse approximations, namely, mine rules based on the positive approximation (MRBPA) and mine rules based on the converse approximation (MRBCA), are proposed for rule extraction. The two algorithms are evaluated by several data sets from the UC Irvine Machine Learning Repository. The experimental results show that MRBPA and MRBCA achieve better classification performances than the method based on attribute reduction.     

Necessary optimality conditions for fuzzy variational problems

The main aims of this study are to derive the fuzzy Euler-Lagrange conditions for both fuzzy unconstrained and constrained variational problems based on the concepts of differentiability and integrability of a fuzzy mapping that may be parameterized by the left and right-hand functions of its α-level sets.     

Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.