[1]耿亚南,邓计才.基于人工鱼群优化SVM的声磁标签信号检测研究[J].郑州大学学报(工学版),2017,38(04):35-38,83.[doi:10.13705/j.issn.1671-6833.2017.04.001]
　Deng Jicai,Geng Yanan.Improved AFSA Optimization of SVM in The Application of Magnetic EAS Acoustic Signal Detection[J].Journal of Zhengzhou University (Engineering Science),2017,38(04):35-38,83.[doi:10.13705/j.issn.1671-6833.2017.04.001]
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基于人工鱼群优化SVM的声磁标签信号检测研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38
期数:
2017年04期
页码:
35-38,83
栏目:
出版日期:
2017-07-18

文章信息/Info

Title:
Improved AFSA Optimization of SVM in The Application of Magnetic EAS Acoustic Signal Detection
作者:
耿亚南; 邓计才
郑州大学信息工程学院,河南郑州,450001
Author(s):
Deng Jicai;  Geng Yanan
School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, 450001
关键词:
人工鱼群算法; 支持向量机; 声磁标签; 检测率; 实时检测
Keywords:
DOI:
10.13705/j.issn.1671-6833.2017.04.001
文献标志码:
A
摘要:
为了提高声磁EAS系统的检测率,增强系统抗干扰性,研究了一种改进人工鱼群算法(IAFSA)与支持向量机(SVM)相结合的声磁标签信号检测算法(IAFSA-SVM).分析了支持向量机和传统人工鱼群算法的优势和缺陷,并提出了改进方案.实验表明:改进人工鱼群算法相比人工鱼群算法、遗传算法和粒子群算法收敛速度更快、寻优精度更高;IASFA-SVM算法相比传统的声磁标签检测算法体现出了检测率高、检测距离远和误报率低等优势,并且可以满足系统实时检测要求.
Abstract:
In order to improve the detection rate of the acoustic magnetic EAS system,and enhance the antiinterference performance,the paper studied a new label detection algorithm that was the combination of the improved artificial fish swarm algorithm (IAFSA) and the support vector machine (SVM).An improved scheme was proposed after analyzing the strengths and weaknesses of the traditional AFSA and SVM.The experimentalresults showed that the IASFA had the faster rate of convergence and the higher accuracy than AFSA,the genetic algorithm and the particle swarm algorithm;The IASFA-SVM had the higher detection rate,the longer detective distance and the lower rate of false than the traditional magnetic label detection algorithm,and the IASFA-SVM also could meet the requirements of real-time detection.

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