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首页> 外文期刊>Mathematical Problems in Engineering >Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition
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Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition

机译：基于粒子群优化的直觉模糊核匹配跟踪。

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摘要

In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm, namely, PS-IFKMP, is proposed. This algorithm is applied to the aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency.
机译：为了克服基于贪婪策略的最优基本函数从冗余基函数字典中搜索直觉模糊核匹配追踪（IFKMP）所导致的训练时间过长的问题，该算法具有强大的全局搜索能力和快速收敛速度用于加快在功能字典中搜索最佳基本功能数据的速度。提出了一种基于粒子群优化算法PS-IFKMP的直觉模糊核匹配追踪方法。该算法应用于需要实时能力的航空航天目标识别。仿真结果表明，与传统方法相比，该算法可减少训练时间，并显着提高计算效率，且分类精度几乎不变，而模型具有更好的稀疏性和泛化性。还证明了该方法适用于需要准确性和效率的应用。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第10期|587925.1-587925.11|共11页
  • 作者

    Yu Xiaodong; Lei Yingjie; Yue Shaohua; Meng Feixiang;

  • 作者单位

    Air Force Engn Univ, Air Def & Antimissile Inst, Xian 710051, Peoples R China.;

    Air Force Engn Univ, Air Def & Antimissile Inst, Xian 710051, Peoples R China.;

    Air Force Engn Univ, Air Def & Antimissile Inst, Xian 710051, Peoples R China.;

    Air Force Engn Univ, Air Def & Antimissile Inst, Xian 710051, Peoples R China.;

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