基于改进KH算法优化ELM的目标威胁估计

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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 assessment; Krill Herd algorithm; extreme learning machine; opposition-based learning; neural networks; weights; thresholds; threat 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.