BP Neural Network Data Fusion algorithm optimized based on adaptive fuzzy particle swarm optimization

Wireless sensor networks (WSN) are currently the subject of scientific research in the world. With the wireless sensor network, it can collect the changes of various monitoring targets to meet the objective requirements of data transmission, signal analysis and signal processing. In order to improve...

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Bibliographic Details
Published in:	2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) pp. 592 - 597
Main Authors:	Yang, Mengjie, Geng, Yushui, Yu, Kun, Li, Xuemei, Zhang, Shudong
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2018
Subjects:	Adaptive systems Biological neural networks Data Fusion Data integration Fuzzy Control Optimization Particle swarm optimization Wireless sensor networks WSN
Online Access:	Get full text
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Summary:	Wireless sensor networks (WSN) are currently the subject of scientific research in the world. With the wireless sensor network, it can collect the changes of various monitoring targets to meet the objective requirements of data transmission, signal analysis and signal processing. In order to improve the energy efficiency of the wireless sensor network and prolong the network lifetime, this paper uses fuzzy control to update the particle position in the algorithm, and proposes a BP Neural Network Data Fusion algorithm optimized based on adaptive fuzzy particle swarm optimization(AFPSOBP) algorithm. The simulation results show that compared with BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle Swarm (GAPSOBP), it can further reduce network traffic, save node energy and prolong network lifetime.
DOI:	10.1109/ITOEC.2018.8740440