吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 907-912.doi: 10.13229/j.cnki.jdxbgxb201503032

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Particle swarm optimization algorithm based on opposite learning for linear phase low-pass FIR filter optimization

SHAO Peng1, 2, WU Zhi-jian1, 2, ZHOU Xuan-yu2   

  1. 1.State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China;
    2.Computer School, Wuhan University, Wuhan 430072, China
  • Received:2013-09-23 Online:2015-05-01 Published:2015-05-01

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Abstract: Finite Impulse Response (FIR) digital filter design is actually the design of unit impulse response coefficients combination, which is essentially a combination of multi-parameter optimization problem. On this basis, an improved particle swarm optimization algorithm based on opposite learning strategy (OPPSO) is applied to design linear phase low-pass FIR digital filter. OPPSO increases the probability to find the optimal solution by solving the inverse solution of the particle position, thus, to find a set of optimal combination of parameters of the filter. Meanwhile, a more realistic sense of zero phase filters close to the ideal filter is introduced into the experiment for comparison. Experimental results show that the proposed algorithm has good optimal results for linear phase low-pass FIR digital filter design.

Key words: artificial intelligence, particle swarm optimization, finite impulse response, digital filter, opposite learning

CLC Number: 

  • TP18
[1] Li K,Liu Y. The FIR window function design based on evolutionary algorithm[C]∥2011 International Conference on Mechatronic Science, Electric Engineering and Computer,Jilin,2011:1797-1800.
[2] 程佩青.数字信号处理[M].北京:清华大学出版社,2009.
[3] Zhao Z K, Gao H Y, Liu Y Q. Chaotic particle swarm optimization for FIR filter design[C]∥2011 International Conference on Electrical and Control Engineering,Yichang,2011:2058-2061.
[4] Ahmad S U,Antoniou A. A genetic algorithm approach for fractional delay FIR filters[C]∥IEEE International Symposium on Circuits and Systems,Island of Kos,2006:2517-2520.
[5] Mastorakis N E,Gonos I F,Swamy M N S. Design of two dimensional recursive filters using genetic algorithms[J]. IEEE Transaction on Circuits and Systems-I: Fundamental Theory and Applications, 2003,50(5):634-639.
[6] Mercier P,Kilambi S M,Nowrouzian B. Optimization of FRM FIR digital filters over CSD and CDBNS multiplier coefficient spaces employing a novel genetic algorithm[J]. Journal of Computers,2007,9(2):20-31.
[7] An-xin Z, Ping L, Jian-jun L. The design of FIR filter based on APA genetic algorithms[C]∥2011 International Conference on Mechatronic Science, Electric Engineering and Computer,Jilin,2011:1118-1121.
[8] 李辉,张安,赵敏,等. 粒子群优化算法在FIR数字滤波器设计中的应用[J]. 电子学报,2005,33(7):1338-1341.
Li Hui, Zhang An, Zhao Min, et al. Particle swarm optimization algorithm for FIR digital filters design[J]. Acta Electronica Sinica,2005,33(7):1338-1341.
[9] Rajib Kar,Durbadal Mandal,Sangeeta Mondal, et al. Craziness based particle swarm optimization algorithm for FIR band stop filter design[J]. Swarm and Evolutionary Computation ,2012,7:58-64.
[10] Mondal S,Chakraborty D,Kar R, et al. Novel particle swarm optimization for high pass FIR filter design[C]∥2012 IEEE Symposium on Humanities, Science and Engineering Research,Kuala Lumpur,2012:413-418.
[11] Kennedy J, Eberhart R. Particle swarm optimization[C]∥Proceedings of IEEE International Conference on Neural Networks,1995,4(2):1942-1948.
[12] Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence[C]∥CIMCA/IAWTI,Vienna, Austria,2005:695-701.
[13] 寇晓丽,刘三阳.基于模拟退火的粒子群算法求解约束优化问题[J].吉林大学学报:工学版,2007,37(1):136-140.
Kou Xiao-li, Liu San-yang. Particle swarm algorithm based on simulated annealing to solve constrained optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2007, 37(1):136-140.
[15] 纪跃波,秦树人,汤宝平. 零相位数字滤波器[J].重庆大学学报:自然科学版,2000,23(16):4-7.
Ji Yue-bo,Qin Shu-ren,Tang Bao-ping. Digital filtering with zero phase error[J]. Journal of Chongqing University(Natural Science Edition),2000,23(16):4-7.
[16] Luitel B, Venayagamoorthy G K. Differential evolution particle swarm optimization for digital filter design[C]∥2008 IEEE Congress on Evolutionary Computation,Hong Kong,2008:3954-3961.
[17] Ababneh J I, Bataineh M H. Linear phase FIR filter design using particle swarm optimization and genetic algorithms[J]. Digital Signal Processing,2008,18(4):657-668.
[18] Sarangi A,Mahapatra R K,Panigrahi S P. DEPSO and PSO-QI in digital filter design[J]. Expert Systems with Applications, 2011,38(9):10966-10973.
[19] Mandal S,Ghoshal S P,Kar R,et al. FIR band stop filter optimization by improved particle swarm optimization[C]∥2011 World Congress on Information and Communication Technologies,Mumbai,2011:699-704.
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