将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在...将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在此基础上,通过计算规则的信赖度和支持度形成不同层次上符合初定阈值的带粗糙集算子的网络规则集,能减少因噪音的影响而产生的多余规则,提高规则产生和实际分类的效率,使所产生的分类规则集大大缩小,提高在使用规则时检索规则的效率。在负荷预测时自上而下逐层搜索规则网直至找出与所给信息相匹配的规则。粗糙集算子反映了规则的重要程度,同时作为选择规则的标准。实际应用表明,该方法能有效去除噪音,提高默认规则的挖掘效率,从而提高负荷预测的精度,具有一定的实用性。展开更多
In paper [2], Mollestad and Skowron propose an algorithm excavating the default regular from the inconsistent data. But,the algorithm can't fitter noises effectively and Its operation is large,needs too much time ...In paper [2], Mollestad and Skowron propose an algorithm excavating the default regular from the inconsistent data. But,the algorithm can't fitter noises effectively and Its operation is large,needs too much time and Its efficiency is lower because its "up and down" searching strategy begins to search unavoidably from the upper layer which includes the most attributes. So,the paper first proposes the method of determining the attribute weight. ON the basis of the method,the paper defines the concept of the weight regular support degree and the concept of the weight regular trust degree and givs the MDWRBR algorithm,which can filter noises effectively and determine the searching direction and the stop condition and can end the operation before the regular mining conducts to the upper layer. So the algorithm reduces the operation and saves time and has some practical value.展开更多
文摘将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在此基础上,通过计算规则的信赖度和支持度形成不同层次上符合初定阈值的带粗糙集算子的网络规则集,能减少因噪音的影响而产生的多余规则,提高规则产生和实际分类的效率,使所产生的分类规则集大大缩小,提高在使用规则时检索规则的效率。在负荷预测时自上而下逐层搜索规则网直至找出与所给信息相匹配的规则。粗糙集算子反映了规则的重要程度,同时作为选择规则的标准。实际应用表明,该方法能有效去除噪音,提高默认规则的挖掘效率,从而提高负荷预测的精度,具有一定的实用性。
文摘In paper [2], Mollestad and Skowron propose an algorithm excavating the default regular from the inconsistent data. But,the algorithm can't fitter noises effectively and Its operation is large,needs too much time and Its efficiency is lower because its "up and down" searching strategy begins to search unavoidably from the upper layer which includes the most attributes. So,the paper first proposes the method of determining the attribute weight. ON the basis of the method,the paper defines the concept of the weight regular support degree and the concept of the weight regular trust degree and givs the MDWRBR algorithm,which can filter noises effectively and determine the searching direction and the stop condition and can end the operation before the regular mining conducts to the upper layer. So the algorithm reduces the operation and saves time and has some practical value.
