Efficient scheme of pole-zero system identification using Particle Swarm Optimization technique

Majhi, Babita; Panda, G. K.; Choubey, Arvind

doi:10.1109/cec.2008.4630836

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…The authors have shown improved modelling accuracy in terms of mean square error over the ones based on CSO , PSO (Chen and Luk, 2010;Majhi et al, 2008), QPSO (Fang et al, 2006), MuQPSO (Fang et al, 2010b), DE (Karaboga, 2005), ABC (Karaboga, 2009), GSA (Rashedi et al, 2011), SOA (Dai et al, 2010), PSO-QI (Luitel and Venayagamoorthy, 2010), AIW-PSO (Yu et al, 2009) and real coded GA. In all the examples, the authors have shown that HSA require a higher computational time than PSO.…”

Section: Iir System Identification and Filter Designmentioning

confidence: 95%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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Section: Iir System Identification and Filter Designmentioning

confidence: 95%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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“…Optimized coefficient values obtained over five best independent runs for four optimization techniques are presented in Table 2 (Example I, Case 1), Table 5 (Example I, Case 2), 3,6,9,12,15,18,21,24,27,30,33,36,39,42 and 45. Algorithm convergence characteristics for all examples are also shown in Figs.…”

Section: Example IXmentioning

confidence: 99%

“…Conventional Particle swarm optimization (PSO) has mimicked the behavior of bird flocking or fish schooling. [11][12][13][14][15][16][17][18][19] In Quantum behaved PSO (QPSO), quantum behavior of particles in a potential well is adopted in conventional PSO algorithm. 20,21 In PSO with Quantum Infusion (PSO-QI), a hybridized version of PSO and QPSO in which the fast convergence property of PSO and the convergence to a lower average error property obtained by QPSO have been combined to enhance the performance.…”

Section: Introductionmentioning

confidence: 99%

“…32 PSO is a swarm intelligence-based algorithm developed by Eberhart et al 33,34 Several attempts have been taken towards the system identification problem with basic PSO and its modified versions. [11][12][13][14][15][16][19][20][21][22][23][24] The key advantage of PSO is its simplicity in computation and a few steps are required in the algorithm. DE algorithm was first introduced by Storn and Price in 1995.…”

Section: Introductionmentioning

confidence: 99%

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Opposition-based differential evolution for IIR system identification problem

Upadhyay

Kar

Mandal

et al. 2014

Int. J. Model. Simul. Sci. Comput.

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Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems. Success achieved for those evolutionary optimization techniques are due to simplicity and flexibility of algorithm structures. In this paper, optimal set of filter coefficients are searched by the evolutionary optimization technique called Opposition-based Differential Evolution (ODE) for solving infinite impulse response (IIR) system identification problem. Opposition-based numbering concept is embedded into the primary foundation of Differential Evolution (DE) technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution. The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle. With this competent population, faster convergence speed and better solution quality are achieved. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation, crossover and selection adopted in the basic DE technique. When tested against standard benchmark examples, for same order and reduced order models, the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed. Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-2 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-4 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-7 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-9 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-10 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only.

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“…Recently Majhi [18] has proposed an efficient scheme of pole-zero system identification using PSO technique. In 2008, an adaptive nonlinear system identification using Comprehensive Learning PSO is proposed by Katari [19].…”

Section: Introductionmentioning

confidence: 99%

Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

Panda

Pradhan

Majhi³

2011

Adaptation, Learning, and Optimization

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Abstract. Derivative based learning rule poses stability problem when used in adaptive plant modeling. In addition the performance of these techniques deteriorates when used for non-linear plant modeling. In this chapter, the plant modeling task is formulated as an optimization problem. A recently introduced evolutionary algorithm, cat swarm optimization (CSO), is used to develop a new population based learning rule for the model. Adaptive modeling of a benchmarked plant is carried out through simulation study. The performance of the CSO in presence of nonlinearity in the plant is also studied. The results demonstrate superior performance of the CSO compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based approaches for adaptive modeling.

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Efficient scheme of pole-zero system identification using Particle Swarm Optimization technique

Cited by 16 publications

References 9 publications

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Opposition-based differential evolution for IIR system identification problem

Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

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