灰色神经网络在传感器数据融合中的应用研究  

Application of Grey Neural Network in Data Fusion of Sensor

作　　者：程满玲[1] 

机构地区：[1]武汉商学院,武汉430000

出　　处：《激光杂志》2016年第5期104-107,共4页Laser Journal

基　　金：湖北省教育厅科学技术研究计划指导性项目(B2014153)

摘　　要：无线传感器是一种新兴的数据采集技术,为了消除传感器的数据之间冗余,加快通信的速度,提出一种灰色神经网络的传感器数据融合方法。首先对当前无线传感器数据融合的研究进行分析,然后采用多个传感器进行数据实时采集,并采用灰色模型和神经网络对传感器数据进行融合处理,有效去除重复的数据,可以用少量数据反映原始数据特征,最后在NS-2仿真平台上进行验证性。结果表明,本文方法降低传感器节点的通信能耗,降低了数据信息传送量,从而延长了无线传感器网络的寿命。Wireless sensor is an new datcl collection technology. In order to eliminate the redundant data of the sensor and accelerate the speed of communication,sensor data fusion method based on grey neural network is presented.Firstly,carries on the analysis and research on the current wireless sensor data fusion,and after the sensor data acquisition,grey model and neural network are used to fuse the sensor data,the duplicate data is remored,can reflect the features of raw data with a small amount of data,finally,the performance are tested on NS-2 simulation platform. The results show that the proposed method can reduce the communication energy consumption of sensor nodes and reduce the amount of data transmission to prolong the lifetime of wireless sensor network.

关 键 词：传感器网络 灰色模型 神经网络 数据融合 

分 类 号：TN293[电子电信—物理电子学]