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浙江大学学报(工学版)
浙江大学学报(工学版)  2018, Vol. 52 Issue (4): 680-686    DOI: 10.3785/j.issn.1008-973X.2018.04.010
自动化技术     
基于灰狼算法与小波神经网络的目标威胁评估
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
Target threat assessment using grey wolf optimization and wavelet neural network
FU Wei-yang1, 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
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摘要：

为了提高目标威胁度评估的精确度，建立反向学习灰狼算法（OGWO）优化小波神经网络的目标威胁评估模型（OGWO-WNN），提出基于该模型的算法.该模型使用反向学习策略（OBL）优化灰狼算法（GWO），通过改进后的灰狼算法优化小波神经网络的各权值和小波基函数的平移因子与伸缩因子，使优化后的小波神经网络能够对威胁度测试样本集作更好的预测.实验结果显示，采用反向学习灰狼算法能够更好地优化小波神经网络的权值与平移、伸缩因子，使建立的小波神经网络目标威胁评估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评估.
Abstract:

Opposition-based learning grey wolf optimization (OGWO) and wavelet neural network model (OGWO-WNN) was established in order to improve the air targets threat estimation accuracy, and the algorithm based on the model was proposed. Opposition-based learning (OBL) was adopted to optimize grey wolf optimization (GWO) algorithm, and OGWO was employed to simultaneously optimize the weights and translation and scalability factors in WNN. Target threat database was adopted to test the performance of OGWO-WNN in target threat prediction. The experimental results show that the weights and translation and scalability factors in WNN can be better optimized. The air target threat assessment model has higher prediction precision and better generalization ability, and can accurately and effectively complete target threat estimation.
收稿日期: 2017-07-27
CLC:  TP391  
基金资助:

江苏省自然科学基金资助项目（BK20160162）..
通讯作者: 刘以安,男,教授.orcid.org/0000-0003-4989-9400.     E-mail: lya_wx@jiangnan.edu.cn
作者简介: 傅蔚阳(1993-),男,硕士生,从事人工智能的研究.orcid.org/0000-0002-4399-6819.E-mail:18806186287@163.com
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引用本文:

傅蔚阳, 刘以安, 薛松. 基于灰狼算法与小波神经网络的目标威胁评估[J]. 浙江大学学报(工学版), 2018, 52(4): 680-686.

FU Wei-yang, LIU Yi-an, XUE Song. Target threat assessment using grey wolf optimization and wavelet neural network. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(4): 680-686.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.04.010    或    http://www.zjujournals.com/eng/CN/Y2018/V52/I4/680

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