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采用综合学习粒子群算法的有限冲激响应数字滤波器设计 被引量:5

Design of Finite Impulse Response Digital Filter by Comprehensive Learning Particle Swarm Optimization
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摘要 针对标准粒子群优化算法在求解复杂多模问题时容易陷入局部极值点和有限冲击响应数字滤波器(FIR DF)设计时减少误差的问题,将综合学习粒子群优化算法(CLPSO)应用于FIR DF设计中.CLPSO在每一代更新中采用所有粒子全局最优值代替粒子本身的个体历史最优值,当粒子停止更新时,重置粒子最优值,保证粒子学习最优和在错误方向上花费最少计算时间.数值结果显示,在满足算法复杂度、计算时间、逼近误差等设计指标的前提下,CLPSO在低通和高通频率采样法FIR DF设计中比传统查表法、遗传算法和标准粒子群优化算法具有一定的优势. Because standard particle swarm optimization is prone to fall into local extreme points in solving complex multi-mode, and for reducing the error in the digital filter design of finite impulse response (FIR DF), the comprehensive learning particle swarm optimization algorithm is applied to designing FIR DF. In each updating generation, the global optimum values of all particles are taken instead of the individual history optimal one. When the particle updating stops, the optimal value of the particle gets reset to ensure particle to learn best with shortest computing period in wrong directions. The numerical results show the advantages in the low-pass and high-pass frequency sampling FIR filter design, over the traditional look-up table method, genetic algorithm and standard particle swarm optimization algorithm for the same design requirements, such as the calculating task and algorithm complexity.
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2012年第8期71-75,共5页 Journal of Xi'an Jiaotong University
基金 国家"863计划"资助项目(2005AA133070) 电子信息产业发展基金资助项目(XDJ2-0514-27)
关键词 综合学习粒子群算法 滤波器 频率采样 遗传算法 comprehensive learning particle swarm optimization filter frequency sampling genetic algorithm
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  • 1PORKASIGJ,MANOLAKISDG.数字信号处理:原理、算法与应用[M].张晓林,译.北京:电子工业出版社,2004:343-456. 被引量:1
  • 2吴镇扬..数字信号处理[M],2004.
  • 3LU Hung-Ching, TZENG Shian-Tang. Design of two- dimensional FIR digital filters for sampling structure conversion by genetic algorithm approach[J]. Signal Processing, 2000,80(8) : 1445-1458. 被引量:1
  • 4陈小平,于盛林.FIR滤波器设计:基于遗传算法的频率采样技术[J].南京航空航天大学学报,2000,32(3):276-281. 被引量:12
  • 5李成,李飞.改进量子遗传算法及其在FIR滤波器设计中的应用[J].计算机工程与应用,2009,45(4):239-241. 被引量:6
  • 6Huang Wan-Ping, ZHOU Li-Fang, QIAN Ji-Xin. FIR filter design: frequency sampling filters by particle swarm optimization algorithm [ C]//Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Piscataway, NJ, USA: IEEE, 2004: 2322-2327. 被引量:1
  • 7LIGHTSTONE M, MITRA S K, LIN Ing-Song, et al. Efficient frequency-sampling design of one-and two-di- mensional FIR filters using structural sub band decom- position[J]. IEEE Transactions on Circuits and Sys- tems: II Analog and Digital Signal Processing, 1994, 41(3):189-201. 被引量:1
  • 8Wang Xiaohua & He YigangCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, P. R. China,College of Electrical and Information Engineering, Changsha University of Science and Technology,Changsha 410077, P. R. China.Optimal design study of high order FIR digital filters based on neural network algorithm[J].Journal of Systems Engineering and Electronics,2004,15(2):115-119. 被引量:2
  • 9魏辉如,崔琛,王粒宾.基于神经网络的线性相位FIR滤波器设计[J].计算机工程与应用,2009,45(23):82-84. 被引量:3
  • 10EBERHART R C, KENNEDY J. A new optimizer using particle swarm theory[C] // Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Piscataway, NJ, USA: IEEE, 1995: 39-43. 被引量:1

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