Filter approximation using explicit time and frequency domain specifications

Abstract

We demonstrate that enhanced particle swarm optimization (PSO) can be successfully used to evolve high performance filter approximations. These evolved approximations use sets of quantitative specifications which conventional analytically derived approximations can not directly employ. The conventional derivations use only a subset of the quantitative specifications in their algorithm and the remaining specifications are side-effect results of the algorithm. Thus, with enhanced PSO, instead of a filter designer having access to a limited set of " specification knobs" that directly and indirectly achieve performance, a designer has a "knob" for each specification that consequently drives the approximation to desired performance. Our future vision to do performance space modeling for filters is also discussed.

Evolutionary algorithms based analog filter approximation and implementation

“…Studies in recent years show that filter transfer functions with less approximation error has been obtained using optimization techniques compared to conventional methods [4][5][6][7][8][9][10][11][12]. In these studies, several programming methods [4][5][6][7][8] and evolutionary algorithms [9][10][11][12] are proposed to overcome the approximation problem.…”

“…In these studies, several programming methods [4][5][6][7][8] and evolutionary algorithms [9][10][11][12] are proposed to overcome the approximation problem. Sequential quadratic [4], nonlinear [5], linear [6] and semi definite programming [7,8] are considered for analog filter approximation.…”

“…Sequential quadratic [4], nonlinear [5], linear [6] and semi definite programming [7,8] are considered for analog filter approximation. In [9], Optim-filt system, which is based on particle swarm optimization (PSO), is proposed for evolving filter approximations which provides the coefficients of a low-pass filter transfer function. PSO and artificial bee colony (ABC) optimization are investigated for approximation problem with a cost function considering only flatness in the passband and stopband in [10].…”

“…Differential evolution (DE), harmony search (HS), PSO and ABC algorithms are used for optimizing coefficients of a low-pass filter transfer function [11]. Difference between [9] and [10] is the selection of the cost function (CF). In [12], ABC and PSO algorithms are used for high-pass filter approximation and successful results are obtained.…”

A new design method based on artificial bee colony algorithm for digital IIR filters

“…The classical examples of swarm: bees swarming around their hive; a colony of ants; a flock of birds; and an immune system which is a swarm of cells and a crowd that is a swarm of people. Recently, particle swarm optimization algorithm has been introduced for numerical optimization problems [20] and successfully applied to digital filter design and other real-world problems [21,22]. PSO algorithm that is a populationbased stochastic optimization technique models the social behaviour of bird flocking or fish schooling [20] and is well adapted to the optimization of nonlinear functions in multidimensional space.…”