Particle Swarm Optimization incorporating a Preferential Velocity-Updating Mechanism and Its Applications in IIR Filter Design

2006 ◽  
Author(s):  
Heng-Chou Chen ◽  
Oscal T.-C. Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Iir Filter

scholarly journals Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Supriya Dhabal ◽  
Palaniandavar Venkateswaran
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Digital Filters ◽  
Filter Design ◽  
Particle Swarm ◽  
Computational Time ◽  
Two Dimensional ◽  
Basic Algorithm ◽  
Swarm Optimization ◽  
Iir Filter

We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

scholarly journals The Design Of Intelligent Filter For EEG

2018 ◽  
Vol 232 ◽  
pp. 04023
Author(s):  
Dachao Yue ◽  
Haikuan Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Filter Design ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Iir Filter ◽  
Data Processing Method ◽  
Eeg Data ◽  
Filtering Effect

EEG data processing method is usually digital filter designed by the traditional method. Its disadvantage is the transition zone is wide and the filtering effect is poor. Using an improved particle swarm optimization algorithm on IIR digital filters design, the performances of filters designed by various methods are compared and analyzed. Experiments illustrate particle swarm optimization algorithm is effective in IIR filter design and its performance is promising.

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