[1]陈修辉,孙铭霞.基于HKBFO优化神经网络的悬索桥动载识别[J].延边大学学报(自然科学版),2015,41(03):257-260.
　CHEN Xiuhui,SUN Mingxia.Identification of moving load on the suspension bridge based on HKBFO optimized neural network[J].Journal of Yanbian University,2015,41(03):257-260.
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基于HKBFO优化神经网络的悬索桥动载识别

《延边大学学报(自然科学版)》[ISSN:1004-4353/CN:22-1191/N] 卷: 第41卷 期数: 2015年03期 页码: 257-260 栏目: 应用科学研究 出版日期: 2015-07-30
Title:
Identification of moving load on the suspension bridge based on HKBFO optimized neural network
作者:
陈修辉1;  孙铭霞2
1.青岛理工大学 琴岛学院, 山东 青岛 266106; 2.长春光华微电子设备工程中心有限公司, 吉林 长春 130000
Author(s):
CHEN Xiuhui1;  SUN Mingxia2
1.Qingdao College, Qingdao Technological University, Qingdao 266106, China; 2.Changchun Guanghua Micro-Electronics Equipment Engineering Center Co. Ltd., Changchun 130000, China
关键词:
分层菌王觅食算法;  神经网络;  悬索桥;  动载识别
Keywords:
Hierarchical King Bacteria Foraging Optimization algorithm(HKBFO);  neural network;  suspension bridge;  identification of moving load
分类号:
TU312+.1
文献标志码:
A
摘要:
为提高BP神经网络对桥梁动载识别的效果,提出了一种基于分层菌王觅食算法(HKBFO)的桥梁动载识别方法.该算法首先进行“交叉”复制操作,再类比人大代表选举过程进行分层寻优.数值模拟证明,HKBFO算法优于BFO算法,可用于桥梁动载识别.
Abstract:
In order to improve the effect of BP neural network on the moving load identification of bridge, a new method of moving load identification method based on the Hierarchical King Bacteria Foraging Optimization algorithm(HKBFO)is proposed. The algorithm first carried on the “cross” copy operation, and then by the analogy of the election process of the People’s Congress conducted hierarchical optimization. The numerical simulation showed that HKBFO is better than BFO, and can be used for bridge moving load identification.

参考文献/References:

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备注/Memo

收稿日期: 2015-07-21作者简介: 陈修辉(1983—),男,讲师,研究方向为运动稳定性与控制.

更新日期/Last Update: 2015-07-30