Modified artificial bee colony optimisation based FIR filter design with experimental validation using field‐programmable gate array

Abstract

Optimisation based design of finite impulse response (FIR) filters has been an active area of research for quite some time. The various algorithms proposed for FIR filter design aim at meeting a set of desired specifications in the frequency domain. Evolutionary algorithms have been found to be very effective for FIR filter design because of the non‐linear, non‐differentiable and non‐convex nature of the associated optimisation problem. The present work proposes two modified versions of a recently developed evolutionary technique i.e. artificial bee colony (ABC) algorithm for design of FIR filters. The applicability of the proposed approach has been evaluated by comparing its response with conventional reported filter design techniques. The proposed variants of ABC are found to outperform other non‐convex algorithms in achieving the desired specifications. In addition to the simulation results, the designed filters have been implemented in hardware using Xilinx‐xc7vx330t‐3ffg1157 (Virtex‐7) field programmable gate array. The hardware implementation allows validation of the proposed techniques for practical filtering applications by considering real time operation parameters.

Differential Evolution and Local Search based Monarch Butterfly Optimization Algorithm with Applications

“…Unfortunately, this will lead to a large quantization error. Therefore, bionic algorithms are emerged to further search the best fixed-point coefficients of FIR filter 29,30 . In this paper, the proposed algorithm is employed to deal with the fixed-point operation and the results are compared with five existing methods, including P-SO, MBO and three truncation methods.…”

Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

“…The improper determination of model parameters due to the improper selection of the optimization method can lead to an incorrect evaluation of model quality. The used methods were genetic algorithm (GA) [21][22][23][24][25][26], differential evolution (DE) with three different strategies, which are DE/rand/1/exp, DE/rand/2/exp, DE/best/1/bin [27][28][29][30][31][32][33][34][35][36], teaching-learning-based optimization (TLBO) [37][38][39][40][41][42][43][44][45] and artificial bee colony (ABC) [46][47][48][49][50][51][52][53][54][55]. If an inappropriate solving method is used, the adequacy of the mathematical expression will not be determined correctly.…”

A Theoretical Question in the Optimal Design of Matrix Decomposition Based FIR Filter

“…Finite impulse response (FIR) filters [1]- [25] can achieve strict linear-phase (LP) and have guaranteed stability. They are widely used in digital signal processing (e.g., filtering and Hilbert transformer design [9]) and communication systems (e.g., pulse shaping [10] and equalizer).…”