RECOGNITION OF BOLT QUALITY BASE ON ELMAN NEURAL NETWORK BY ANT COLONY OPTIMIZATION ALGORITHM

被引：0

作者：

Di, Wei-Guo ^{[1
,2
]}

Sun, Xiao-Yun ^{[1
]}

Wang, Ming-Ming ^{[1
]}

Xing, Hui ^{[1
]}

Liu, Jing-Na ^{[1
]}

机构：

[1] Shijiazhuang Tiedao Univ, Sch Elect & Elect Engn, Shijiazhuang 050043, Hebei, Peoples R China

[2] Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Hebei, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2018年

基金：

中国国家自然科学基金;

关键词：

Bolt anchoring system; Defect recognition; Elman neural network; Ant colony algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The quality and working condition of the bolt anchoring system determine the safety performance of the whole construction project to a large extent. In order to ensure the effect of the bolt in the support system engineering and prevent the occurrence of major disasters, testing of the bolt quality becomes particularly important. The paper presents a way of identifying the bolt quality that use ant colony algorithm to optimize Elman neural network. The weights and thresholds of Elman neural network are optimized by using the ant colony algorithm and the Elman neural network model to recognition bolt quality is established. The results show that the Elman neural network by ant colony algorithm has better recognition effect and higher prediction precision than the Elman neural network and the Elman neural network by genetic algorithm.

引用

页码：210 / 214

页数：5

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