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This paper presents general finite impulse response (FIR) digital filter design with asymmetric coefficients to approximate passband and stopband magnitude responses and constant passband group delay specifications using an evolutionary optimization algorithm called the Interactive Self Learning Algorithm (ISLA). Lowpass and bandpass digital filters are chosen and their design results are shown to...
This paper presents a design of general finite impulse response (FIR) digital filters using a multiobjective Cuckoo Search Algorithm (MOCSA). The MOCSA is applied to optimize the filter coefficients of FIR lowpass and bandpass digital filters. Design results show that slightly smaller peak passband and stopband magnitude errors and smaller peak passband group delay error can be obtained when compared...
A method for minimax design of linear phase finite impulse response (FIR) digital filters using cuckoo search algorithm (CSA) is presented. Lowpass and bandpass digital filters are used as filter examples. Equiripple linear phase FIR digital filter design results indicate that the peak errors in passband and stopband(s) obtained using the cuckoo search algorithm are similar to those obtained by the...
Artificial bee colony (ABC) algorithm is a swarm based meta-heuristic algorithm inspired by the foraging behavior of honey bees. Due to its simplicity, the ABC algorithm is used for minimax design of linear phase FIR fullband digital differentiators in this paper. Results in term of peak error obtained from designed digital differentiator examples indicate that the approach can reach smaller peak...
In this paper, teaching-learning-based optimization (TLBO) is used for minimax design of linear phase finite impulse response (FIR) digital Hilbert transformers. TLBO is a population-based and heuristic search algorithm which is parameter-free and exhibits a strong convergence ability. The results obtained from using TLBO to design Type 4 linear phase FIR highpass digital Hilbert transformers indicate...
This paper presents an optimal design of linear phase digital finite impulse response (FIR) low pass (LP) filter using Adaptive Particle Swarm Optimization (APSO). APSO is an improved version of conventional PSO in which estimation of evolution state determines one of the four phases such as exploration, exploitation, convergence and jump-out for the entire optimization process. These modifications...
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