查询结果:   陈飞彦,田宇驰,胡亮.物联网中基于KNN和BP神经网络预测模型的研究[J].计算机应用与软件,2015,32(6):127 - 129,202.
中文标题
物联网中基于KNN和BP神经网络预测模型的研究
发表栏目
网络与通信
摘要点击数
1015
英文标题
STUDY ON KNN AND BP NEURAL NETWORK-BASED PREDICTION MODEL IN IOT
作 者
陈飞彦 田宇驰 胡亮 Chen Feiyan Tian Yuchi Hu Liang
作者单位
吉林大学计算机科学与技术学院 吉林 长春 130012     
英文单位
College of Computer Science and Technology,Jilin University,Changchun 130012,Jilin,China     
关键词
物联网 BP神经网络 K近邻 预测模型
Keywords
Internet of Things BP neural network KNN Prediction model
基金项目
国家自然科学基金项目(61073009);国家高技术研究发展计划项目(2011AA010101)
作者资料
陈飞彦,硕士生,主研领域:物联网。田宇驰,硕士生。胡亮,教授。 。
文章摘要
物联网中,无线传感器网络由于环境、资源等因素的变化和限制,会导致部分数据异常或丢失,使数据传输的可靠性降低。因此常用的BP神经网络方法在根据最终获取数据进一步处理时的准确性不高。提出K近邻算法和BP神经网络相结合的二阶段预测模型,先使用K近邻算法对BP神经网络输入数据中异常或缺失数据进行估值和替换预处理,同时进行初步预测,然后将预处理后的数据输入BP神经网络,综合BP神经网络和KNN的预测结果给出最终结论。实际环境中实验表明,所提出的模型能够有效地提高物联网环境中预测的准确度和稳定性。
Abstract
In Internet of Things (IoT), wireless sensor network (WSN) may have part of data abnormal or lost caused by the change and limit in environment, resources and other factors, thus the reliability of data transmission is reduced. So the BP neural network method commonly used is not high in accuracy when further processing according to the final acquisition data. We propose a two-step prediction model which combines k-nearest neighbour (KNN) algorithm and BP neural network. First, it uses KNN algorithm on BP neural network input data to estimate and replace the abnormal and missing data in it as preprocessing, and gives a preliminary prediction at the same time. Then it uses the preprocessed data to input in BP neural network, and provides the final conclusion by integrating the prediction results of BP neural network and KNN. The experiment in actual environment shows that the proposed model can effectively improve the readiness and stability of prediction in IoT environment.
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