Optimal Linear Phase Finite Impulse Response Band Pass   Filter Design Using Craziness
Based Particle Swarm   Optimization Algorithm

doi:10.1007/s12204-011-1213-5

摘要/Abstract

摘要： An efficient method is proposed for the
design of finite impulse response (FIR) filter with arbitrary pass
band edge, stop band edge frequencies and transition width. The
proposed FIR band stop filter is designed using craziness based
particle swarm optimization (CRPSO) approach. Given the filter
specifications to be realized, the CRPSO algorithm generates a set
of optimal filter coefficients and tries to meet the ideal frequency
response characteristics. In this paper, for the given problem, the
realizations of the optimal FIR band pass filters of different
orders have been performed. The simulation results have been
compared with those obtained by the well accepted evolutionary
algorithms, such as Parks and McClellan algorithm (PMA), genetic
algorithm (GA) and classical particle swarm optimization (PSO).
Several numerical design examples justify that the proposed optimal
filter design approach using CRPSO outperforms PMA and PSO, not only
in the accuracy of the designed filter but also in the convergence
speed and solution quality.

关键词: finite impulse response (FIR) filter, particle
swarm optimization (PSO), craziness based particle swarm
optimization (CRPSO), Parks and McClellan algorithm (PMA), genetic
algorithm (GA), optimization

Abstract: An efficient method is proposed for the
design of finite impulse response (FIR) filter with arbitrary pass
band edge, stop band edge frequencies and transition width. The
proposed FIR band stop filter is designed using craziness based
particle swarm optimization (CRPSO) approach. Given the filter
specifications to be realized, the CRPSO algorithm generates a set
of optimal filter coefficients and tries to meet the ideal frequency
response characteristics. In this paper, for the given problem, the
realizations of the optimal FIR band pass filters of different
orders have been performed. The simulation results have been
compared with those obtained by the well accepted evolutionary
algorithms, such as Parks and McClellan algorithm (PMA), genetic
algorithm (GA) and classical particle swarm optimization (PSO).
Several numerical design examples justify that the proposed optimal
filter design approach using CRPSO outperforms PMA and PSO, not only
in the accuracy of the designed filter but also in the convergence
speed and solution quality.

Key words: finite impulse response (FIR) filter, particle
swarm optimization (PSO), craziness based particle swarm
optimization (CRPSO), Parks and McClellan algorithm (PMA), genetic
algorithm (GA), optimization

中图分类号:

TN713

SANGEETA Mandal, SAKTI Prasad Ghoshal, RAJIB Kar, . Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness
Based Particle Swarm Optimization Algorithm[J]. 上海交通大学学报（英文版）, 2011, 16(6): 696-703.

SANGEETA Mandal, SAKTI Prasad Ghoshal, RAJIB Kar, . Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness
Based Particle Swarm Optimization Algorithm[J]. Journal of shanghai Jiaotong University (Science), 2011, 16(6): 696-703.

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