属性集约简论文_赵慧材

导读:本文包含了属性集约简论文开题报告文献综述、选题提纲参考文献及外文文献翻译，主要关键词:粗糙,集约,信息系统,舒适性,属性,模糊,规则。

属性集约简论文文献综述

赵慧材^[1]（2014）在《采用模糊粗糙集约简属性的支持向量机短期负荷预测方法》一文中研究指出With the deepening of the process of electricity market, to meet the safe operation of the power system, the prerequisite for reliable power system operation economy put forward higher requirements. As a short-term load forecasting system to order electric power dispatching department plans and arrangements important reference operating mode, the system is improved operating economy plays an important role. Because of the many factors that affect short-term load, coupled with the interference of random factors, to short-term load forecasting has brought no small difficulty. So, how reasonable and effective use of these factors, how to characterize the complex nonlinear relationship between different factors and load are two important issues currently facing.Article detailed analysis of fuzzy rough sets and support vector machines (SVM) characteristics of these two methods of data mining, given the former is able to extract data from a large number of implicit, there is the potential value of the decision-making information to effectively deal with the problem of information redundancy, the latter due to a strong non-linear fitting ability and good generalization performance and is widely used in load forecasting. This paper proposes a hybrid data mining method with fuzzy rough sets and SVM, and used to power the short-term load forecasting; This method uses fuzzy rough set theory attribute reduction algorithm to solve the power load of the many factors information expansion problems, eliminate irrelevant factors and decision information, and after the reduction factor as SVM input. The proposed method in the overall consideration of many factors affecting the load at the same time compressing the appropriate input variables eliminating the previous modeling process input variables selected based on the impact of subjective experience.In addition, the article also in all aspects of the work load forecasting for clues pretreatment on historical data, select SVM model and kernel function SVM model parameter optimization of several aspects were discussed. Pretreatment of historical data including correction of outliers, to fill the missing values of discrete and continuous data standardization sample data processing, these steps provide support for accurate data on load forecasting; SVM model nuclear select the function to predict the impact on the performance of the model is large, the analysis of the kernel function is relatively common, choose the RBF kernel function with good analytical nature; of issues that need to optimize the model parameters when creating SVM model, using genetic algorithm SVM model parameter optimization, prediction model based on SVM establish optimal parameters. Numerical example of the predictive effect of combining fuzzy rough set reduction SVM model and the conventional SVM model was not carried reduction compared to verify the effectiveness of the proposed method.(本文来源于《长沙理工大学》期刊2014-04-01）

陈广胜,李广鹏,于海鹏,陈文帅^[2]（2011）在《木质材料舒适性属性的粗糙集约简与模糊评价》一文中研究指出应用粗糙集模糊评价法对10种常见木质建材的"舒适性"属性进行指标约简和评价。首先初选出影响木质材料"舒适性"的14个因素指标;其次针对所选因素构建粗糙集模型,将因素集约简至5个指标;然后确定各种材料在各因素的隶属函数及隶属度,进行模糊综合评价,得出各材料的舒适性排序;最后对评价结果进行雷达图分析和分组比较。结果表明:这种方法能够为人们合理选择和利用木质材料提供科学依据。(本文来源于《林业科学》期刊2011年08期）

闫德勤,迟忠先^[3]（2003）在《一种实值属性信息系统的粗集约简方法》一文中研究指出本文研究应用粗集理论对实值信息系统属性进行约简的方法 .对实值属性信息系统进行约简的根本问题是如何对实值属性离散化 .通过对离散化方法与属性约简的关系进行研究 ,提出了实值属性离散化的一种自动确定属性类别的方法 ,并结合粗集理论给出了对实值属性信息系统约简的算法 .用所提出的算法进行了实验 ,并给出了实验结果(本文来源于《小型微型计算机系统》期刊2003年03期）

属性集约简论文开题报告

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应用粗糙集模糊评价法对10种常见木质建材的"舒适性"属性进行指标约简和评价。首先初选出影响木质材料"舒适性"的14个因素指标;其次针对所选因素构建粗糙集模型,将因素集约简至5个指标;然后确定各种材料在各因素的隶属函数及隶属度,进行模糊综合评价,得出各材料的舒适性排序;最后对评价结果进行雷达图分析和分组比较。结果表明:这种方法能够为人们合理选择和利用木质材料提供科学依据。

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