Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (10): 3024-3028.DOI: 10.11772/j.issn.1001-9081.2017.10.3024

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Missile hit prediction model based on adaptively-mutated chaotic particle swarm optimization and support vector machine

XU Lingkai, YANG Rennong, ZHANG Binchao, ZUO Jialiang   

  1. College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China
  • Received:2017-04-10 Revised:2017-06-14 Online:2017-10-10 Published:2017-10-16
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (71501184).


许凌凯, 杨任农, 张彬超, 左家亮   

  1. 空军工程大学 航空航天工程学院, 西安 710038
  • 通讯作者: 许凌凯(1993-),男,湖北鄂州人,硕士研究生,主要研究方向:机器学习、智能空战,
  • 作者简介:许凌凯(1993-),男,湖北鄂州人,硕士研究生,主要研究方向:机器学习、智能空战;杨任农(1968-),男,四川彭州人,教授,博士,主要研究方向:航空兵任务规划和作战效能评估;张彬超(1993-),男,陕西西安人,硕士研究生,主要研究方向:深度学习、智能空战;左家亮(1987-),男,陕西西安人,博士,主要研究方向:航空兵任务规划和作战效能评估.
  • 基金资助:

Abstract: Intelligent air combat is a hot research topic in military aviation field and missile hit prediction is an important part of intelligent air combat. Aiming at the shortcomings of insufficient research on missile hit prediction, poor optimization ability of the algorithm, and low prediction accuracy of the model, a missile hit prediction model based on Adaptively-Mutated Chaotic Particle Swarm Optimization (AMCPSO) and Support Vector Machine (SVM) was proposed. Firstly, feature extraction of air combat data was carried out to build sample library for model training; then, the improved AMCPSO algorithm was used to optimize the penalty factor C and the kernel function parameter g in SVM, and the optimized model was used to predict the samples; finally, comparison tests with classical PSO algorithm, the BP neural network method and the method based on lattice were made. The results show that the global and local optimization ability of the proposed algorithm are both stronger, and the prediction accuracy of the proposed model is higher, which can provide a reference for missile hit prediction research.

Key words: Support Vector Machine (SVM), Adaptively-Mutated Chaotic Particle Swarm Optimization (AMCPSO), missile hit prediction, intelligent air combat, military aviation

摘要: 针对国内外关于导弹命中预测方面存在的研究深度不足、算法寻优能力不强、模型预测精度不高等缺陷,提出一种基于自适应变异混沌粒子群算法(AMCPSO)和支持向量机(SVM)的导弹命中预测模型。首先,对空战数据进行特征提取,构建模型训练所需样本库;然后,采用改进的AMCPSO算法对SVM中的惩罚因子C和核函数参数g进行寻优,并用优化后的模型对样本进行预测;最后,与经典PSO算法、BP神经网络法、网格法构建的预测模型进行了对比实验。实验结果表明,所提算法的全局寻优能力与局部寻优能力均得到提高,模型预测精度较高,可为导弹命中预测研究提供一定的参考依据。

关键词: 支持向量机, 自适应变异混沌粒子群优化, 导弹命中预测, 智能空战, 军事航空

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