摘要
为了提高测量的准确性和快捷性,需要融合处理多传感器检测的数据。本文首先介绍BRF网络的特性和训练方式,然后进行样本数据采集、样本数据归一化、神经网络的训练及其结构的确定,完成基于RBF网络的水分检测数据处理过程,实现粮食水分检测中的多传感数据融合。经过Matlab中的神经网络模型训练后,实验结果表明,拟合值始终在目标值上下波动,波动的范围在7%以内,该方法具有较大的优越性,可在其它工业领域中推广应用。
For improving the accuracy and speed of measurement,it needs to deal with the data from multi-sensors.Firstly,this paper introduces the characters and training mode of BRF network,and then describes the whole processes,which includes data collection,data normalization,neural network training and network structure selection,using multi-sensors to detect the grain moisture based on BRF network.Lastly,trained by the net models in Matlab,the results prove that the fitted value fluctuates around target values and the differentials are less than 7%.This method has advantages and can be applied in other industry fields.
出处
《计算机与现代化》
2011年第6期103-107,共5页
Computer and Modernization
基金
河南省社科联调研课题(SKL-2010-2982)
