[1]傅蔚阳,刘以安,薛松.基于改进KH算法优化ELM的目标威胁估计[J].智能系统学报,2018,13(05):693-699.[doi:10.11992/tis.201704007]
 FU Weiyang,LIU Yian,XUE Song.Target threat assessment using improved Krill Herd optimization and extreme learning machine[J].CAAI Transactions on Intelligent Systems,2018,13(05):693-699.[doi:10.11992/tis.201704007]
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基于改进KH算法优化ELM的目标威胁估计(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年05期
页码:
693-699
栏目:
出版日期:
2018-09-05

文章信息/Info

Title:
Target threat assessment using improved Krill Herd optimization and extreme learning machine
作者:
傅蔚阳1 刘以安1 薛松2
1. 江南大学 物联网工程学院, 江苏 无锡 214122;
2. 中国船舶重工集团公司第七研究院 电子部, 北京 100192
Author(s):
FU Weiyang1 LIU Yi’an1 XUE Song2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Electronic Department, The Seventh Research Institute of China Shipbuilding Industry Corporation, Beijing 100192, China
关键词:
目标威胁估计磷虾群算法极限学习机反向学习神经网络权值阈值威胁估计模型
Keywords:
target threat assessmentKrill Herd algorithmextreme learning machineopposition-based learningneural networksweightsthresholdsthreat estimation model
分类号:
TP391.9
DOI:
10.11992/tis.201704007
摘要:
为了提高目标威胁度估计的精确度,建立了反向学习磷虾群算法(OKH)优化极限学习机的目标威胁估计模型(OKH-ELM),提出基于此模型的算法。该模型使用反向学习策略优化磷虾群算法,并通过改进后的磷虾群算法优化极限学习机初始输入权重和偏置,使优化后的极限学习机能够对威胁度测试样本集做更好的预测。实验结果显示,OKH算法能够更好地优化极限学习机的权值与阈值,使建立的极限学习机目标威胁估计模型具有更高的预测精度和更强的泛化能力,能够精准、有效地实现目标威胁估计。
Abstract:
To improve the accuracy of target threat estimation, the opposition-based learning Krill Herd optimization (OKH) and extreme learning machine (OKH-ELM) model is established, and the algorithm based on the model is presented. The OKH-ELM adopts opposition-based learning (OBL) to optimize KH, and then the improved KH and extreme learning machine are employed to simultaneously optimize the initial input weights and offsets of the hidden layer in ELM. A target threat database is adopted to test the performance of OKH-ELM in target threat prediction. The experimental result shows that OKH Algorithm can better optimize the weights and thresholds of the hidden layer in ELM and improve the prediction precision and generalization ability of the target threat assessment model; therefore, it can accurately and effectively estimate target threat.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-04-12。
基金项目:江苏省自然科学基金项目(BK20160162).
作者简介:傅蔚阳,男,1993年生,硕士研究生,主要研究方向为雷达对抗、人工智能;刘以安,男,1963年生,教授,博士,主要研究方向为数据融合与数据挖掘、雷达对抗、模式识别与智能系统。主持或参与教育部、国防科工委、江苏省教育厅等省部级项目5项。发表学术论文60余篇;薛松,男,1987年生,工程师,主要研究方向为信号与信息处理、内场仿真系统设计。发表学术论文2篇。
通讯作者:傅蔚阳.E-mail:18806186287@163.com.
更新日期/Last Update: 2018-10-25