一种基于容错的感知数据回归模型研究

传感器主要用于对外部环境进行监测,然而当传感器发生故障时监测结果会出现误差。为了提高传感器发生故障时系统的容错能力,提出了一种容错的感知数据回归模型。首先,对最小二乘和岭回归两种线性回归模型进行分析,并分析了线性回归模型的相关统计量；然后,分析了部分传感器发生故障时系统的相关统计量,并以此为基础分析了协变量矩阵的上下界；最后,依据协变量矩阵定义了故障指标,并将优化模型转化为同时最小化故障指标和均方误差的问题。实验表明,提出的容错回归模型与传统的最小二乘法和岭回归方法相比具有更小的预测误差,因而当传感器发生故障时所提模型具有更好的健壮性。

Fault Tolerant Regression Model for Sensing Data

Sensors are always used to monitor external environment,whenever faults occur in sensors,there will be prediction errors.In order to improve the ability of fault tolerance when faults occur,this paper proposed a fault tolerant regression model for sensing data.Firstly,we analyzed linear models including least squares and ridge regression,and also analyzed statistics of regression model.Secondly,we analyzed the related statistics when sensors failed,and analyzed the lower and upper bounds of covariate matrix based on these statistics.Finally,we defined fault index based on covariate matrix,and transformed the optimum of model into minimize fault index and mean square error simultaneously.The experiments show that the proposed fault tolerant model has lower error than traditional least squares and ridge regression,and thus has better robustness when sensors fail.