Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Abstract

The design of finite impulse response (FIR) filters involves the estimation of effective filter coefficients, making the designed filter exhibit infinite stopband attenuation and have a flat-shaped passband. The few conventional filter design methods such as impulse response truncation (IRT) and windowing technique exhibit undesirable characteristics owing to the Gibbs phenomenon, thus making them unsuitable for various practical complexities. This research work employs the fuzzy logic-based diversity-controlled self-adaptive differential evolution algorithm (FLDCSaDE) for the design of FIR band stop (BS) and high pass (HP) filters. In order to validate the results of the proposed technique, various population-based evolutionary computing techniques such as the covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), self-adaptive differential evolution (SaDE), and Jaya algorithm have also been applied for determining the effective filter coefficients. The performance of the various algorithms has been analysed and compared based on the parameters such as stopband attenuation, passband attenuation, and ripples. The simulation results show that the FLDCSaDE algorithm outperforms other evolutionary algorithms having 4% and 1.5% lower ripples than the SaDE algorithm for high pass and band stop filters, respectively. Experimental results depict that the performance of the fuzzy approach causes positioning and tracking accuracy obtained to be improved by 27% and the corresponding false positive rate (FPR) is substantially reduced to 0.11 from the mean amplitude value obtained from the fuzzy approach in the frequency response. The frequency response obtained from the FLDCSaDE algorithm is close to the ideal response of the BS and HP FIR filters.