Information fusion algorithm based on improved particle swarm BP neural network in WSN

doi:10.7523/j.issn.2095-6134.2020.05.012

Abstract

Abstract: Back-propagation (BP) neural network has low convergence speed, is sensitive to the initial value, and easily falls into the local optimal solution in data fusion algorithms in wireless sensor network (WSN). To solve these problems, a data fusion algorithm based on improved particle swarm optimization BP neural network in WSN (BSO-BP) is proposed. The beetle antennae search(BAS) algorithm is used to improve the particle swarm optimization. Then the imporved particle swarm optimization is used to optimize the BP neural network weights and thresholds, which are applied to WSN data fusion. The cluster head nodes extract the feature of the collected data by optimizing the trained BP neural network, and send the merged data to the sink node. Simulation results show that BSO-BP algorithm effectively improves the fusion accuracy and convergence speed, decreases the redundant data communication and prolongs network lifetime. BSO-BP algorithm reduces the relative error by 12.4% and the root-mean-square error by 11%, compared to BP and PSO-BP algorithms.

Key words: wireless sensor networks (WSN), data fusion, BP neural network, particle swarm optimization, beetle antennae search algorithm

CLC Number:

TN929.5

WANG Hong, XU Youyu, TAN Chong, LIU Hong, ZHENG Min. Information fusion algorithm based on improved particle swarm BP neural network in WSN[J]. , 2020, 37(5): 673-680.

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