科技创新与应用2024，Vol.14Issue(4)：28-32,38,6.DOI:10.19981/j.CN23-1581/G3.2024.04.007
基于SSA优化BP神经网络的易燃易爆气体分类算法研究
摘要
Abstract
The technology of detecting explosives by various sensors has been used to detect different types of explosives under various conditions,but the same type of explosives identification and classification technology is almost not available,and the traditional BP neural network model convergence speed is slow,easy to fall into the local optimal value and other problems.In this paper,PWCLM chaotic mapping and Gaussian mutation operator are used to improve the Sparrow search algorithm(SSA),optimize the initial weight of the model,and identify and classify six kinds of flammable and explosive gases.Finally,this model is compared with BP,SSA-BP,WOA-BP and other models,and three evaluation indexes are used to evaluate the three models.The results show that ISA-BP model has high stability and better recognition accuracy than other models.The classification accuracy of ISA-BP model,recall rate and F1 index were 99.01% ,99.12% and 99.12% respectively.关键词
麻雀搜寻算法/PWCLM混沌映射/高斯变异算子/BP神经网络/易燃易爆气体Key words
Sparrow Search Algorithm(SSA)/PWCLM chaotic mapping/gaussian mutation operator/BP neural network/flammable and explosive gas分类
信息技术与安全科学引用本文复制引用
万成炜,李捷..基于SSA优化BP神经网络的易燃易爆气体分类算法研究[J].科技创新与应用,2024,14(4):28-32,38,6.基金项目
The Natural Science Foundation of Hubei Province under Grant(2022CFB941) （2022CFB941）
