函数S-粗集与规律F-隐藏

函数单向S-粗集(Function one direction singular rough sets)是用R-函数等价类定义的,函数是个规律;函数单向S-粗集具有规律特征、动态特征.利用函数单向S-粗集,给出规律F-隐藏概念,提出规律的F-隐藏定理,隐藏识别准则,给出规律的F-隐藏的应用.规律的F-隐藏是函数S-粗集中的一个新的应用研究方向,函数S-粗集是信息规律研究中的一个新理论与新工具.     

直觉模糊S-粗决策模型及应用

基于直觉模糊S-粗集理论,提出双向直觉模糊S-粗决策模型.首先给出指标值的量化方法,将目标矩阵进行标准化处理.其次,建立直觉模糊S-粗决策的上一决策和下一决策优化模型,并给出直觉模糊S-粗决策算法及详细步骤.最后,以空袭目标为例,详细研究了目标威胁程度评估过程.结果表明,直觉模糊S-粗决策模型能够综合处理决策因素的定性与定量因素,得到的决策结果综合性能最优.所得的排序结果真实、准确地反映了实际情况.     

含核Vague区域和Vague洞区域关系及蕴涵定理

模糊区域的空间信息表示和区域关系处理在空间数据库、地理信息系统和人工智能等领域具有重要的意义.引入Vague集的概念和理论对含核Vague区域和Vague洞区域关系进行了系统的研究.基于Vague集给出了含核Vague区域、Vague洞区域和区域划分等概念;详细分析了处于不同平面中的含核Vague区域和Vague洞区域关系;给出了基于Vague集的区域关系交集模型和子区域关系间的蕴涵定理和算法;进一步给出了实例分析.研究成果可较好地处理含核Vague区域和Vague洞区域内的模糊点不确定的隶属信息和确定含核Vague区域与Vague洞区域的关系等问题.     

基于同构化信息融合的异构不确定多属性决策新模型与新算法

不确定多属性决策过程中,现有两大困难:(1)如何较好地表达和处理具有不确定性的属性评价信息;(2)如何将基于多样性评判准则的多准则评价结果进行信息融合,并获得更合理的综合评价结论.基于同构化思想,针对学术界最近才提出的一种能较好地处理具有多信息来源模糊信息的新数学模型"多值直觉模糊集模型",研究了多值直觉模糊集的隶属度与非隶属度的综合评判新课题与新方法;提出了兼有不确定语言型与区间型的异构风险型多属性决策新问题与新模型,构造了基于同构化信息融合的异构不确定多属性决策新模型与新算法.     

Extension of domains of states

We deal with categorical aspects of the extensions of generalized probability measures. In particular, we study various domains of fuzzy sets, describe the relationships between σ-fields of crisp sets and generated Łukasiewicz tribes of measurable functions, and mention some probabilistic aspects. D-posets and sequential continuity play an important role. This work was supported by the Slovak Research and Development Agency under the contract No. APVV-0071-06, Protocol for Scientific and Technological Collaboration between the Republic of Italy and Slovak Republic, Project No. 15: Algebraic and Logical Systems of Soft Computing, and VEGA 2/6088/26.     

A comprehensive decision making structure for partitioning of make-to-order, make-to-stock and hybrid products

Manufacturing systems produce their products by making use of three main strategies: make-to-order, make-to-stock and hybrid (combination of MTO and MTS strategies) systems. Hence, several products must be considered and partitioned in terms of many different conflicting criteria, leading to a large set of subjective or ambiguous data. Thus, an effective evaluation approach is essential to improve decision quality. In this paper a comprehensive decision, making structure is proposed to choose the proper strategy for producing the products. It consists of a novel hybrid methodology, which combines analytical hierarchy process (AHP) and technique for order performance by similarity to ideal solution (TOPSIS) for partitioning of products. In this study, the rating of each alternative regarding different criteria (throughout the TOPSIS technique) is described by means of linguistic variables. Furthermore, fuzzy AHP methodology is applied to determine the weights of evaluation criteria. At the end, the effectiveness of the proposed model is demonstrated through a case study.     

Reducing false positives in anomaly detectors through fuzzy alert aggregation

In this paper we focus on the aggregation of IDS alerts, an important component of the alert fusion process. We exploit fuzzy measures and fuzzy sets to design simple and robust alert aggregation algorithms. Exploiting fuzzy sets, we are able to robustly state whether or not two alerts are “close in time”, dealing with noisy and delayed detections. A performance metric for the evaluation of fusion systems is also proposed. Finally, we evaluate the fusion method with alert streams from anomaly-based IDS.     

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.     

泛函网络神经元构造理论与方法

泛函网络是近年提出的一种对神经网络的有效推广.与神经网络不同,它处理的是一般的泛函模型,它在各个神经元之间的连接没有权值,并且神经元函数不固定的,往往是一给定的基函数的组合,泛函网络学习的目的就是求出神经元函数的精确表达式或近似表达式. 迄今关于泛函网络神经元基函数的存在性和选取方法缺乏理论依据.文中基于Banach空间中偏序理论,分析了泛函网络神经元基函数的存在性,给出了泛函网络神经元基函数选取方法,对于完善泛函网络的基础理论具有参考价值.     

基于圆形约束C-V水平集的肺部CT图像病灶分割

针对肺部CT图像中圆形病灶区域的分割问题,对Chan-Vese水平集图像分割方法进行了分析和改进,提出了基于圆形约束的C-V水平集模型,进而提出了基于圆形约束水平集的肺部图像病灶分割算法,解决了图像中大小不同的多圆检测问题。对合成图像和实际临床肺部CT图像进行了分割实验,结果表明,该方法可以较好地分割出图像中的多个圆形区域,算法具有较好的抗噪性,实现速度较快,有利于实现肺部CT图像肺结节自动检测。