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1)  rough set model algorithm
粗糙集模型算法
1.
The rough set model algorithm based on the decision table was used in project evaluation to acquire common decision knowledge from decisions of many specialists as the criterion and the final result of project evaluation so as to improve the existing evaluation algorithm.
为了改进现有的评估方法,在项目审查(评估)中,采用基于决策表的粗糙集模型算法,从众多专家的决策中找到潜在地存在于各个决策中的、公认的决策共识作为项目审查的依据和最终结果。
2)  rough set model
粗糙集模型
1.
This chapter mostly tells the rough set model under equivalence relation,common relation and probability,gives their definition property and respective kinds of definition type,the relationship between them,and examples.
主要叙述在等价关系、一般关系和概率论中的粗糙集模型,给出它们的定义、性质、各自的几种定义类型和它们之间的联系以及若干例子。
2.
The validity of the algorithm is verified by examples and the important theoretical foundation can then be provided to solve the problem of the rough set model with incomplete information system.
对基于最大相容类的粗糙集模型进行深入的研究,提出两种此模型下的容差类,并从最大相容类的求解算法入手进一步提出了这两种容差类求解的算法。
3)  rough sets reduction algorithm
粗糙集约简算法
1.
Use rough sets reduction algorithm to improve BP neural network load forecasting model;
利用粗糙集约简算法改进BP神经网络负荷预测模型
4)  generalized rough set model
扩展粗糙集模型
1.
A new generalized rough set model is constructed to deal with situations where different data objects have different importance and different attributes have different characteristics using a weighting function for data objects and the attribute characteristic function.
在可变精度粗糙集的基础上 ,构造了一种新的扩展粗糙集模型。
5)  Bayesian rough set model
Bayesian粗糙集模型
1.
In this paper, the newly proposed Bayesian rough set model is analyzed, and the defect of the model is discussed, further more, a modified form of the model is proposed.
Bayesian粗糙集模型是基于变精度和概率论的思想最新提出的无参数模型。
6)  probabilistic rough set model
粗糙集概率模型
1.
In this paper,the authors have presented an algorithm SRII based on probabilistic rough set model,which eliminates attributes very efficiently and effectively.
论文根据粗糙集概率模型应用于数据挖掘的特点,提出了一种用于数据预处理的基于信息归纳的概率粗糙集算法SRII;实验证明,SRII结合算法C4。
补充资料:模型算法控制
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性质:预测控制算法的一种。采用基于脉冲响应的非参数模型作为内部模型,用过去和未来的输入输出信息,根据内部模型,预测系统未来的输出状态,经过用模型输出误差进行反馈校正,再与设定值的输入轨迹进行比较,应用二次型性能指标进行滚动优化,然后,再计算当前时刻应加于系统的控制动作,完成整个控制循环。

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