Sand-Dust Storm Ensemble Forecast Model Based on Rough Set

To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.     

模糊变异粗集与[α/R]知识

从问题的内、外两方面出发，结合模糊集理论与变异粗糙集理论提出了模糊变异粗集模型，并对模糊变异粗集和模糊变异知识进行了讨论，给出了模糊变异知识及其相关定理。     

基于粗糙集理论的工控界面多智能体协作模式研究

1 Introduction An agent is an autonomous entity that runs in a dy-namic environment . Generally, it describes mentalstates such as knowledge , belief , promise , ability,etc. A multi-agent system ( MAS) consists of severalcomputational agents .It ai ms to transformlarge andcomplicated tasks to small ones that can communicateand cooperate with each other[1-3].Therefore ,cooper-ationis essential in MAS. The rough set theory (RST) was proposed by thePolish Mathematician Pawlak in 1982 .It w…     

带隶属度及权重的粗糙集模型的构建

提出了带隶属度及权重的粗糙集模型。在这种模型中，给出带隶属度及权重的信息系统，进行噪音的处理．这种粗糙集的拓广模型克服经典粗糙集分类过于严格、对噪音过于敏感、某些隐藏在边界中的规则丢失等缺陷，并用算例验证了该模型是可行的。     

一种基于概率的覆盖粗糙集模型研究

文章给出了一种基于概率覆盖的上、下近似的定义,并且研究了它的性质、数字特征、拓扑特征等,为研究覆盖粗糙集提供了新的模型.     

多种数据挖掘技术相结合的应用实例分析

利用限制性关联规则挖掘技术与粗糙集理论挖掘技术相结合,挖掘教学信息表,去除无关联属性后,最终得出影响教师教学效果的各因素及其各自的影响程度。     

浅谈桥上CRTSⅡ型轨道板粗铺技术

结合京沪高速铁路土建一标段十八局管段轨道板粗铺施工,从施工程序、施工工艺等方面进行探讨。     

基于覆盖粗糙集的动态数据库的知识发现

在覆盖粗糙集理论框架下,给出并证明了处理动态数据库的两个定理,提供了知识发现的一个新工具。     

基于粗糙集与PSO-SVM的二甲醚催化燃烧预测模型

影响二甲醚催化燃烧的因素较多,并且影响因素之间又相互作用,将支持向量机引入二甲醚预测领域.为了克服LS-SVM的局限性,提出将粗糙集理论、粒子群优化算法以及SVM算法相结合,对LS-SVM的惩罚因子和径向基函数的参数进行优化选取.对二甲醚催化燃烧进行了实例研究.结果表明,所提出的模型提高了网络的预测精度,验证了该模型的有效性.