In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.
B. Luitel and G. K. Venayagamoorthy, "Differential Evolution Particle Swarm Optimization for Digital Filter Design," Proceedings of the IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), Institute of Electrical and Electronics Engineers (IEEE), Jun 2008.
The definitive version is available at https://doi.org/10.1109/CEC.2008.4631335
IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
Electrical and Computer Engineering
Keywords and Phrases
Design; FIR Filters; Digital Arithmetic; Digital Filters; Evolutionary Algorithms; Filter Banks; Function Evaluation; Health; Impulse Response; Numerical Analysis; Optimization; Probability Density Function; Wave Filters
Article - Conference proceedings
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 2008