FPGA based implementation of a genetic algorithm for ARMA model parameters identification

Abstract

In this paper, we propose an FPGA implementation of a genetic algorithm (GA) for linear and nonlinear auto regressive moving average (ARMA) model parameters identification. The GA features specifically designed genetic operators for adaptive filtering applications. The design was implemented using very low bit-wordlength fixed-point representation, where only 6-bit wordlength arithmetic was used. The implementation experiments show high parameters identification capabilities and low footprint.