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基于粒子群优化算法思想的组合自适应滤波算法

林川 冯全源

林川, 冯全源. 基于粒子群优化算法思想的组合自适应滤波算法[J]. 电子与信息学报, 2009, 31(5): 1245-1248. doi: 10.3724/SP.J.1146.2008.00407
引用本文: 林川, 冯全源. 基于粒子群优化算法思想的组合自适应滤波算法[J]. 电子与信息学报, 2009, 31(5): 1245-1248. doi: 10.3724/SP.J.1146.2008.00407
Lin Chuan, Feng Quan-yuan. Combined Adaptive Filtering Algorithm Based on the Idea of Particle Swarm Optimization[J]. Journal of Electronics and Information Technology, 2009, 31(5): 1245-1248. doi: 10.3724/SP.J.1146.2008.00407
Citation: Lin Chuan, Feng Quan-yuan. Combined Adaptive Filtering Algorithm Based on the Idea of Particle Swarm Optimization[J]. Journal of Electronics and Information Technology, 2009, 31(5): 1245-1248. doi: 10.3724/SP.J.1146.2008.00407

基于粒子群优化算法思想的组合自适应滤波算法

doi: 10.3724/SP.J.1146.2008.00407

Combined Adaptive Filtering Algorithm Based on the Idea of Particle Swarm Optimization

  • 摘要: 根据粒子群优化(PSO)算法的社会心理学指导思想并结合自适应FIR滤波器的特点,设计了合适的惯性项、认知项与社会项表达式,并将之应用于组合自适应滤波器的子自适应滤波器更新中,提出了基于PSO算法思想的组合自适应滤波算法,分析了新算法的计算复杂度。理论分析与不同条件下的自适应系统辨识仿真结果表明,新算法可以在不明显提高计算量的条件下较好地平衡自适应滤波器的稳态失调与跟踪能力,其收敛性能优于其它几种较新的LMS算法。
  • 罗小东, 贾振红, 王强. 一种新的变步长LMS 自适应滤波算法[J]. 电子学报, 2006, 34(6): 1123-1126.Luo Xiao-dong, Jia Zhen-hong, and Wang Qiang. A newvariable step size LMS adaptive filtering algorithm[J]. ActaElectronica Sinica, 2006, 34(6): 1123-1126.[2]林川, 冯全源. 模糊步长LMS 算法及其性能分析[J]. 系统工程与电子技术, 2007, 29(6): 967-970.Lin Chuan and Feng Quan-yuan. Fuzzy step size LMSalgorithm and its performance analysis[J]. SystemsEngineering and Electronics, 2007, 29(6): 967-970.[3]Aboulnasr T and Mayyas K. A robust variable step-sizeLMS-type algorithm: analysis and simulations[J].IEEETrans. on Signal Processing.1997, 45(3):631-639[4]谷源涛, 唐昆, 崔慧娟等. 独立假设下的最优变步长LMS 模型和算法[J]. 中国科学(E 辑), 2003, 33(8): 760-768.Gu Yuan-tao, Tang Kun, and Cui Hui-juan, et al.. Optimalvariable step size LMS model and algorithm based on theindependence assumption[J]. Science in China (Series E),2003, 33(8): 760-768.[5]谷源涛, 唐昆, 崔慧娟. 步长选择定理及其应用[J]. 中国科学(E 辑), 2003, 33(10): 947-953.Gu Yuan-tao, Tang Kun, and Cui Hui-juan, et al.. Step sizeselection theorem and its applications[J]. Science in China(Series E), 2003, 33(10): 947-953.[6]Martinez-Ramon M, Arenas-Garcia J, and Navia-Vazquez A,et al.. An adaptive combination of adaptive filters for plantidentification[C]. 14th International Conference on DigitalSignal Processing, Piscataway: IEEE Press, 2002: 1195-1198.[7]Arenas-Garcia J, Gmez-Verdejo V, and Figueiras-Vidal A R.New algorithms for improved adaptive convex combination ofLMS transversal filters[J].IEEE Trans. on Instrumentationand Measurement.2005, 54(6):2239-2249[8]Arenas-Garcia J, Figueiras-Vidal A R, and Sayed A H. Meansquareperformance of a convex combination of two adaptivefilters[J].IEEE Trans. on Signal Processing.2006, 54(3):1078-1090[9]Shi Y and Eberhart R C. A modified particle swarmoptimizer[C]. Proceedings of the IEEE InternationalConference on Evolutionary Computation. IEEE Press,Piscataway, NJ, 1998: 69-73.[10]林川, 冯全源. 基于微粒群本质特征的混沌微粒群优化算法[J]. 西南交通大学学报, 2007, 42(6): 665-669.Lin Chuan, Feng Quan-yuan. Chaotic particle swarmoptimization algorithm based on the essence of particleswarm[J]. Journal of Southwest Jiaotong University, 2007,42(6): 665-669.[11]Krusienski D J and Jenkins W K. Design and performance ofadaptive systems based on structured stochastic optimizationstrategies[J].IEEE Circuits and Systems Magazine.2005,5(1):8-20
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出版历程
  • 收稿日期:  2008-04-08
  • 修回日期:  2008-09-05
  • 刊出日期:  2009-05-19

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