基于粒子群优化的直觉模糊核匹配追踪算法

doi:10.3969/j.issn.0372-2112.2015.07.009

电子学报 ›› 2015, Vol. 43 ›› Issue (7): 1308-1314.DOI: 10.3969/j.issn.0372-2112.2015.07.009

基于粒子群优化的直觉模糊核匹配追踪算法

余晓东¹, 雷英杰¹, 岳韶华¹, 何颖²

1. 空军工程大学防空反导学院, 陕西西安 710051;
2. 空军95133部队, 湖北武汉 430415

收稿日期:2014-06-02 修回日期:2014-10-11 出版日期:2015-07-25
- 作者简介:
- 余晓东 男,1989年6月出生于江西九江.现为空军工程大学计算机应用技术专业博士生,主要研究方向为智能信息处理与信息融合. E-mail:agosoa@163.com;雷英杰 男,1956年11月出生于陕西华阴.IEEE高级会员.现为空军工程大学教授、博士生导师,主要研究方向为智能信息处理与智能决策. E-mail:leiyjie@163.com
- 基金资助:
- 国家自然科学基金 (No.61272011,No.61309022); 陕西省自然科学青年基金 (No.2013JQ8031)

Research of PSO-Based Intuitionistic Fuzzy Kernel Matching Pursuit Algorithm

YU Xiao-dong¹, LEI Ying-jie¹, YUE Shao-hua¹, HE Ying²

1. Air and Missile Defense College, Air Force Engineering University, Xi'an, Shaanxi 710051, China;
2. Air Force 95133, Wuhan, Hubei 430415, China

Received:2014-06-02 Revised:2014-10-11 Online:2015-07-25 Published:2015-07-25
- Supported by:
- National Natural Science Foundation of China (No.61272011, No.61309022); Youth Fund of Natural Science Foundation of Shaanxi Province (No.2013JQ8031)

摘要/Abstract

摘要：

针对现有直觉模糊核匹配追踪算法采用贪婪算法搜索最优基函数而导致学习时间过长的问题,汲取了粒子群优化算法全局搜索能力强、收敛速度快的优势对最优基函数的搜索过程进行优化,提出了一种基于粒子群优化的直觉模糊核匹配追踪算法,并将该算法应用于时效性要求更高的空天目标识别领域.实验结果表明,与传统方法相比,本文方法在识别率相当的情况下有效缩短一次匹配追踪时间,计算效率明显提高,且所得模型具有稀疏性好,泛化能力高等优点,特别适用于兼顾识别率和实时性的应用领域.

关键词: 直觉模糊集, 核匹配追踪, 粒子群优化, 贪婪算法

Abstract:

In order to overcome the long learning time caused by searching optimal basic function data 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.And 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 aero 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 leaving the classification accuracy almost unchanged,while the model has better sparsity and generalization.It is also demonstrated that this approach is much suitable to the application requiring both accuracy and efficiency.

Key words: intuitionistic fuzzy set, kernel matching pursuit, particle swarm optimization, greedy algorithm

中图分类号:

TP182
TP391

余晓东, 雷英杰, 岳韶华, 等. 基于粒子群优化的直觉模糊核匹配追踪算法[J]. 电子学报, 2015, 43(7): 1308-1314.

YU Xiao-dong, LEI Ying-jie, YUE Shao-hua, et al. Research of PSO-Based Intuitionistic Fuzzy Kernel Matching Pursuit Algorithm[J]. Acta Electronica Sinica, 2015, 43(7): 1308-1314.

参考文献

[1] Vincent Pascal,Bengio Yoshua.Kernel matching pursuit[J].Machine Learning,2002,48:165-187.
[2] Cevher V,Krause A.Greedy dictionary selection for sparse representation[J].IEEE Journal of Selected Topics Signal Processing,2011,5(5):979-988.
[3] Sun P,Yao X.Sparse approximation through boosting for learning large scale kernel machines[J].IEEE Transactions on Neural Networks,2010,21(6):883-894.
[4] 缑水平,焦李成,张向荣.基于免疫克隆的核匹配追踪集成图象识别算法[J].模式识别与人工智能,2009,22(1):79-85. Gou S P,Jiao L C,Zhang X R.Image recognition with kernel matching pursuit classifier ensemble based on immune clone[J].Pattern Recognition and Artificial Intelligence,2009,22(1):79-85.(in Chinese)
[5] Jianwu Li,Yao Lu.Refining kernel matching pursuit[J].Lecture Notes in Computer Science,2010,6064(1):25-32.
[6] 李青,焦李成,周伟达.基于模糊核匹配追寻的特征模式识别[J].计算机学报,2009,32(8):1687-1694. Li Qing,Jiao Li-cheng,Zhou Wei-da.Pattern recognition based on the fuzzy kernel matching pursuit[J].Chinese Journal of Computers,2009,32(8):1687-1694.(in Chinese)
[7] 雷阳,雷英杰,周创明.基于直觉模糊核匹配追踪的目标识别方法[J].电子学报,2011,39(6):1441-1446. Lei Y,Lei Y J,Zhou C M.Techniques for target recognition based on intuitionistic fuzzy kernel matching pursuit[J].Acta Electronica Sinica,2011,39(6):1441-1446.(in Chinese)
[8] 付丽华,李宏伟,张猛.基于更贪心策略的快速正交核匹配追踪算法[J].电子学报,2013,41(8):1580-1585. Fu L H,Li H W.Fast orthogonal kernel matching pursuit based on greedier strategy[J].Acta Electronica Sinica,2013,41(8):1580-1585.(in Chinese)
[9] 王国富,张海如,张法全,徐婷.基于改进遗传算法的正交匹配追踪信号重建方法[J].系统工程与电子技术,2011,33(5):974-977. WANG G F,Zhang H R,Zhang F Q,Xu T.Orthogonalmatching pursuit signal reconstruction based on improved genetic algorithm[J].Systems Engineering and Electronics,2011,33(5):974-977.(in Chinese)
[10] 李恒建,尹忠科,王建英.基于量子遗传优化算法的图像稀疏分解[J].西南交通大学学报,2007,42(1):19-23. Li H J,Yin Z K,Wang J Y.Image sparse decomposition based on quantum genetic algorithm[J].Journal of Southwest Jiaotong University,2007,42(1):19-23.(in Chinese)
[11] 雷阳,孔韦韦,雷英杰.基于直觉模糊c均值聚类核匹配追踪的弹道中段目标识别方法[J].通信学报,2012,33(11):136-143. Lei Y,Kong W W,Lei Y J.Technique for target recognition based on intuitionistic fuzzy c-means clustering and kernel matching pursuit[J].Journal on Communications,2012,33(11):136-143.(in Chinese)
[12] V P Thafasal Ijyas,S M Sameer.Low complexity metaheuristics for joint ML estimation problems[J].Applied Mathematics and Computation,2014,230:342-358.
[13] Rana S,Jasola S,Kumar R.A review on particle swarm optimization algorithms and their applications to data clustering[J].Artificial Intelligence Review,2011,35(3):211-222.
[14] 冯德军,王博,王伟.弹道中段目标雷达识别研究进展综述[J].中国电子科学研究院学报,2013,8(2):142-147. Feng D J,Wang B,Wang W.Overview of progress in midcourse radar target recognition[J].Journal of CAEIT,2013,8(2):142-147.(in Chinese)

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基于粒子群优化的直觉模糊核匹配追踪算法

Research of PSO-Based Intuitionistic Fuzzy Kernel Matching Pursuit Algorithm

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