摘要
在工业、农业等实际应用领域中，传感器是获取信息的主要工具，而当某个传感器发生故障时，单个决策系统的性能会急剧下降，甚至导致整个系统的瘫痪．为了提高决策系统的容错能力，将多分类器融合的方法应用到此领域．首先利用粗糙集的方法进行特征选择，得到3组不同的约简．再利用模糊输出支持向量机方法，训练出3个分类器．测试样本通过这3个分类器分别给出该样本属于各类的隶属度，再通过均值的融合方法，给出最后的分类决策．将该SVM多分类器融合方法应用于6组UCI标准数据集中，实验表明，传感器出现断路或短路故障时，单个分类器的分类精度急剧下降，但是通过融合的方法，使得最后的分类精度与原始精度基本相当．
In some application fields of industry and agriculture, sensor is the main tool for getting information. When some sensor is wrong, the performance of a single decision system will get down sharply, even all the system will collapse, In order to improve the fault tolerance, the multi-classifiers fusion method is introduced into this field. Firstly, 3 different reductions were gotten through rough sets. Then with fuzzy output SVM method, 3 classifiers were trained. The membership to each class of every testing sample was gotten through the above 3 classifiers. With the average value fusion method, the final class was given. In the experiment of 6 UCI standard data sets, it shows when some sensor is open or short, the performance of the single classifier will get down, while with the fusion method, the accuracy is comparable to the original accuracy.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2006年第B07期389-392,共4页
Journal of Harbin Engineering University
关键词
多分类器融合
支持向量机
容错
传感器故障
multi-classifiers fusion
support vector machines
fault tolerance
sensor fault
作者简介
谢宗霞（1981-），女，博士生；
于达仁（1966-），男，教授．
