[1]安世博,何 勇,孟亚茹.基于深度信念网络的家居设备状态预测模型[J].计算机技术与发展,2019,29(08):161-166.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 031]
 AN Shi-bo,HE Yong,MENG Ya-ru.State Prediction Model of Smart Home Devices Based on Deep Belief Network[J].,2019,29(08):161-166.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 031]
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基于深度信念网络的家居设备状态预测模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年08期
页码:
161-166
栏目:
应用开发研究
出版日期:
2019-08-10

文章信息/Info

Title:
State Prediction Model of Smart Home Devices Based on Deep Belief Network
文章编号:
1673-629X(2019)08-0161-06
作者:
安世博;?何 勇;?孟亚茹
贵州大学 计算机科学与技术学院,贵州 贵阳 550000
Author(s):
AN Shi-bo;?HE Yong;?MENG Ya-ru
School of Computer Science and Technology,Guizhou University,Guiyang 550000,China
关键词:
智能家居;?深度信念网络;?网络预测模型;?自动化控制
Keywords:
smart home;?deep belief network;?network prediction model;?automatic control
分类号:
TP183
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 08. 031
摘要:
由于不同的智能家居设备之间的独立性以及通讯方式的差异,增大了用户使用和管理方面的难度。为了解决智能家居设备在自动化控制方面的难题,提出了一种基于深度信念网络的网络预测模型。该模型首先针对单个设备采用基于受限玻尔兹曼机的深度置信网络构建设备模型,通过无监督预训练逐层地挖掘设备通用化特征,最终采用有监督 BP 神经网络作为常规拟合层,综合考虑多个独立设备进行构建和训练网络预测模型。经过训练,该模型能够预测智能设备工作状态,根据预测结果调整相应设备,实现对智能家居设备的预测。该模型对智能设备工作状态的预测准确率达到 97%,并在收敛速度以及设备数量影响方面具备较好的预期效果。 实验结果表明,该模型能够充分发掘智能设备状态和用户信息间的联动关系,实现后续对设备的智能化自动控制。
Abstract:
Due to the independence of different smart home devices and the difference in communication methods,the difficulty in user usage and management is increased. In order to solve the problem of intelligent home equipment in automation control,we propose a network prediction model based on deep belief network. The model firstly uses a deep Boltzmann-based deep confidence network to construct a device model for a single device. The unsupervised pretraining layer-by-layer mining device generalization features are used,and finally the supervised BP neural network is used as a conventional fitting layer,considering multiple independent devices to build and train network prediction models. After training,the model can predict the working state of the smart device,adjust the corresponding device according to the prediction result,and realize the prediction of the smart home device. The model has a prediction accuracy of 97% for the working state of smart devices,and has an expected effect on the convergence speed and the number of devices.The experiment shows that the model can fully explore the linkage relationship between the state of the intelligent device and the user information,and realize the intelligent automatic control of the device.

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更新日期/Last Update: 2019-08-10