| 基于改进灰狼算法优化BP神经网络的无线传感器网络数据融合算法 |
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| 作者姓名： | 曹轲 谭冲 刘洪 郑敏 |
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| 作者单位： | 1.上海微系统与信息技术研究所 中国科学院无线传感网与通信重点实验室, 上海 200050;2.中国科学院大学, 北京 100049 |
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| 基金项目： | 中国科学院青年创新促进会（2018269）和上海市科委科研计划项目（18511106400）资助 |
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| 摘 要： | 为提高无线传感器网络数据融合精度,降低网络能耗,延长网络生存时间,提出基于改进灰狼算法优化BP神经网络的无线传感器网络数据融合算法(IGWOBPDA).首先为平衡灰狼算法全局与局部搜索能力提出改进控制参数和动态权重更新位置的改进灰狼方案,利用改进灰狼算法对BP神经网络初始阈值和初始权值进行优化以解决数据融合中BP神经网...
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| 关 键 词： | 无线传感器网络 数据融合 BP神经网络 灰狼算法 控制参数 权重因子 |
| 收稿时间： | 2020-01-06 |
| 修稿时间： | 2020-04-08 |
Data fusion algorithm of wireless sensor network based on BP neural network optimized by improved grey wolf optimizer |
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| Authors: | CAO Ke TAN Chong LIU Hong ZHENG Min |
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| Institution: | 1.Key Laboratory of Wireless Sensor Networks and Communications of CAS, Shanghai Institute of Microsystem and Information Technology, Shanghai 200050, China;2.University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | In order to improve the accuracy of the fusion data in wireless sensor network, reduce the energy consumption, and extend the network lifetime, data fusion algorithm of wireless sensor network based on improved grey wolf optimizer to optimize BP neural network (IGWOBPDA) is proposed in this paper. Firstly to balance the global and local search capabilties, the improved considering the actual energy consumption and clustering of the wireless sensor network's transmitting nodes, a clustering scheme based on node residual energy parameters and node density parameters is proposed,which adjusts the weighting factors to adapt to the actual situation of the network data fusion transmission process. Compared with BPNDA algorithm and GAPSOBP algorithm, simulation results show that IGWOBPDA algorithm has better data fusion accuracy and convergence in different data sets, which can effectively reduce the amount of data transmission and node energy consumption, extend network survival time, and maintain stability under different network scales. control parameter and the method of dynamic weight update position is proposed in the paper, which aims at the problems that the initial value of BP neural network in wireless sensor network data fusion algorithm is sensitive and the result can easily be the local optimal solution. Secondly, |
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| Keywords: | wireless sensor network data fusion algorithm BP neural network grey wolf optimizer control parameter weighting factor |
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