摘要
为了提高板形模式识别精度,提出了一种基于改进海鸥算法结合Elman网络的板形模式识别方法。将改进的海鸥算法对Elman网络权值阈值进行优化,用于板形模式识别,选取20组数据进行测试,并将结果与基于BP神经网络的板形模式识别和基于传统Elman网络的板形模式识别方法进行比较,结果表明本文算法精度更高、效果更好,均方误差MSE相比其他算法低2个数量级。
In order to improve the accuracy of flatness pattern recognition,a flatness pattern recognition method based on modified seagull optimization algorithm(MSOA)and Elman network is proposed.The weight threshold of Elman network is optimized with MSOA and then used for flatness pattern recognition.20 sets of data are chosen for testing,and the obtained results are compared respectively with the flatness pattern recognition results based on BP neural network and traditional Elman network.It is found that algorithm method proposed in this paper has higher accuracy and better effect,with the mean square error lower than other algorithms by two orders of magnitude.
作者
吕冠艳
田学东
李奋华
LÜGuanyan;TIAN Xuedong;LI Fenhua(Department of Information and Engineering,Shanxi Conservancy Technical Institute,Yuncheng 044000,Shanxi,China;School of Computer Science and Technology,Hebei University,Baoding 071002,Hebei,China;School of Mathematics and Information Technology,Yuncheng University,Yuncheng 044000,Shanxi,China)
出处
《矿冶工程》
CAS
北大核心
2023年第2期140-144,148,共6页
Mining and Metallurgical Engineering
基金
国家自然科学基金青年科学基金(11501498)
山西省教育科学“十四五”规划课题(GH⁃21060)。
关键词
海鸥算法
混沌映射
板形模式识别
ELMAN神经网络
板形控制
seagull optimization algorithm(SOA)
chaotic map
flatness pattern recognition
Elman neural network
flatness control
作者简介
吕冠艳(1980—),女,山西运城人,硕士,讲师,主要研究方向为数据挖掘、智能仪器与数字媒体技术。
