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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/855

Title: Efficient scheme of pole-zero system identification using Particle Swarm Optimization technique
Authors: Majhi, B
Panda, G
Choubey, A
Keywords: IIR filters
computational complexity
genetic algorithms
mean square error methods
parameter estimation
particle swarm optimisation
poles and zeros
Issue Date: 2008
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation, CEC, June 1-6, Hongkong, 2008. (IEEE World Congress on Computational Intelligence).
Abstract: This paper introduces the application of particle swarm optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational complexity. This paper applies this powerful PSO tool to identify the parameters of standard IIR systems and compares the results with those obtained using the genetic algorithm (GA). The comparative results reveal that the PSO shows faster convergence, involves low complexity, yields minimum MSE level and exhibits superior identification performance in comparison to its GA counterpart.
URI: http://dx.doi.org/10.1109/CEC.2008.4630836
http://hdl.handle.net/2080/855
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