Optimal Design of Low Pass Finite Impulse Response Filter Using Hybrid Population Based Algorithm

Damte, Tsehay (2014) Optimal Design of Low Pass Finite Impulse Response Filter Using Hybrid Population Based Algorithm. Masters thesis, Addis Ababa.

PDF (Optimal Design of Low Pass Finite Impulse Response Filter Using Hybrid Population Based Algorithm) Tsehay Damte.pdf - Accepted Version Restricted to Repository staff only Download (864kB) | Request a copy

Abstract

This research presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using hybrid population based algorithm (the hybrid of genetic algorithm and particle swarm optimization). The basis behind this is that such a hybrid approach is expected to have merits of particle swarm optimization (PSO) with those of genetic algorithm (GA). Applying crossover operation on the PSO, information can be swapped between two particles to have the ability to fly to the new search area. Also the purpose of applying mutation to PSO is to increase the diversity of the population and the ability to have the PSO to avoid the local maxima and hence the solution quality is improved. In the design process, the filter length, passband and stopband frequencies, feasible passband ripple (the difference between an ideal filter and designed approximate filter in the passband reign) and stopband ripple (the difference between an ideal filter and designed approximate filter in the stopband reign) sizes are specified. Evolutionary algorithms like GA, PSO, and hybrid of genetic algorithm and particle swarm optimization (HGAPSO) have been used in this work for the design of linear phase FIR lowpass (LP) filter. In this research filter of order 20, 30, 40, and 50 have been realized using HGAPSO and the simulations clearly indicate that HGAPSO have best performance in terms of passband ripple for orders higher than 20. The results justify that the HGAPSO outperforms GA and PSO in terms of minimum stopband ripple and maximum attenuation.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	FIR Filter, RGA, PSO, HGAPSO, Parks and McClellan (PM) Algorithm, Evolutionary Optimization, Low Pass Filter
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Africana
Depositing User:	Andriamparany Edilbert RANOARIVONY
Date Deposited:	02 Jul 2018 13:33
Last Modified:	02 Jul 2018 13:33
URI:	http://thesisbank.jhia.ac.ke/id/eprint/6440

Actions (login required)

View Item