[1]孙江,行鸿彦,吴佳佳.基于IA-SVM模型的混沌小信号检测方法[J].探测与控制学报,2020,42(03):119.[doi:.]
 SUN Jiang,XING Hongyan,WU Jiajia.Chaotic Small Signal Detection Method Based on IA-SVM Model[J].,2020,42(03):119.[doi:.]
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基于IA-SVM模型的混沌小信号检测方法()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
42
期数:
2020年03
页码:
119
栏目:
出版日期:
2020-06-15

文章信息/Info

Title:
Chaotic Small Signal Detection Method Based on IA-SVM Model
文章编号:
1008-1194(2020)03-0119-07
作者:
孙江12行鸿彦12吴佳佳12
1.南京信息工程大学气象灾害预报预警与评估协同创新中心,江苏 南京210044;2.南京信息工程大学江苏省气象探测与信息处理重点实验室,江苏 南京210044
Author(s):
SUN Jiang12XING Hongyan12WU Jiajia12
1.Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Jiangsu Key Laboratory of Meteorological Detection and Information Processing, Nanjing University of Information Science and Technology , Nanjing 210044, China
关键词:
微弱信号检测免疫算法支持向量机混沌特性
Keywords:
weak signal detection immune algorithm support vector machine chaotic characteristics
分类号:
TN911.7
DOI:
.
文献标志码:
A
摘要:
针对传统微弱信号检测方法在混沌背景下的检测能力较弱,提出了一种基于IA-SVM模型的混沌小信号检测方法。该方法经求嵌入窗构建混沌相空间后,利用免疫算法寻优能力对支持向量机中影响预测精度的三个参数进行优化,从而建立混沌时间序列的预测模型。实验验证结果表明,预测信号的均方根误差为0.000 146 3(信噪比为-104.247 3 dB),较传统微弱信号检测方法有着显著优势。
Abstract:
Aiming at the poor detection ability of traditional weak signal detection method under chaotic background, a chaotic small signal detection method based on IA-SVM model was proposed. The method was constructed by embedding the window to construct the chaotic phase space, the immune algorithm optimization ability was used to optimize the three parameters in the support vector machine that affect the prediction accuracy, so as to establish the prediction model of the chaotic time series. Experimental verification results showed that the root mean square error of the predicted signal was 0.000 146 3 (signal-to-noise ratio is -104.247 3dB), which had significant advantages over the traditional weak signal detection method.

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备注/Memo

备注/Memo:
收稿日期:2019-12-11
基金项目:国家自然科学基金项目资助(61671248,41605121);江苏省重点研发计划项目资助(BE2018719)
作者简介:孙江(1996—),男,江苏南京人,硕士研究生,研究方向:微弱信号检测、仪器仪表。E-mail:1643719315@qq.com。
更新日期/Last Update: 2020-07-15