Linear phase low pass FIR filter design using Improved Particle Swarm Optimization

2011 ◽  
Author(s):  
Saptarshi Mukherjee ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Sangeeta Mondal ◽  
S. P. Ghoshal
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Particle Swarm ◽  
Fir Filter ◽  
Linear Phase ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Low Pass ◽  
Fir Filter Design

Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique

2020 ◽  
pp. 344-358
Author(s):  
Taranjit Kaur ◽  
Balwinder Singh Dhaliwal
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Linear Phase ◽  
Swarm Optimization ◽  
Local Optima ◽  
Response Characteristics ◽  
Low Pass ◽  
Particle Swarm Optimization Technique

This chapter presents a mutation-based particle swarm optimization (PSO) approach for designing a linear phase digital low pass FIR filter (LPF). Since conventional gradient-based methods are susceptible to being trapped in local optima, the stochastic search methods have proven to be effective in a multi-dimensional non-linear environment. In this chapter, LPF with 20 coefficients has been designed. Since filter design is a multidimensional optimization problem, the concept of mutation helps in maintaining diversity in the swarm population and thereby efficiently controlling the local search and convergence to the global optimum solution. Given the filter specifications to be realized, the Mutation PSO (MPSO) tries to meet the ideal frequency response characteristics by generating an optimal set of filter coefficients. The simulation results have been compared with basic PSO and state of artworks on filter design. The results justify that the proposed technique outperforms not only in convergence speed but also in the quality of the solution obtained.

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