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
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