Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm

Dash, Meera; Panigrahi, Trilochan; Sharma, Renu

doi:10.1016/j.jksues.2017.11.002

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…• System 1: First consider a 2nd order IIR system at each sensor. The transfer function is given as (Dash et al, 2017a)…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The noisy output (measured data) of the IIR system at i th instant is represented as d(i) . The measurement is related to the input vector by its constant coefficient difference equation, given as (Widrow and Strearns, 1985;Dash et al, 2017a;Upadhyay et al, 2016)…”

Section: Problem Formulationmentioning

confidence: 99%

“…The current estimate output depends upon N number of previous outputs. The corresponding transfer function of the IIR system is given as (Dash et al, 2017a)…”

Section: Problem Formulationmentioning

confidence: 99%

“…The SE is defined as the sum of square of errors resulted from actual and estimated parameter after learning which is given as (Dash et al, 2017a)…”

Section: Apd and Se Performancementioning

confidence: 99%

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Robust estimation of IIR system's parameter using modified particle swarm optimisation algorithm

Dash¹,

Panigrahi²,

Sharma³

2019

IJCISTUDIES

Self Cite

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This paper introduces a novel method of robust parameter estimation of infinite impulse response (IIR) system. When training signal contains strong outliers, the conventional squared error-based cost function fails to provide desired performance. Thus a computationally efficient robust cost functions are used here. It is a fact that the IIR system falls in local minima. Thus the gradien-based algorithm which is good for finite impulse response (FIR) system, can not be used for IIR. Therefore the parameters of the IIR system is estimated using modified particle swarm optimisation algorithm. The most used and analysed robust cost functions such as Hubers and saturation nonlinearity function are used in the optimisation algorithm. The simulation results show that the proposed robust algorithms are providing better performance than the Wilcoxon norm-based robust algorithm and conventional error squared-based PSO algorithms.

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“…• System 1: First consider a 2nd order IIR system at each sensor. The transfer function is given as (Dash et al, 2017a)…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

“…The current estimate output depends upon N number of previous outputs. The corresponding transfer function of the IIR system is given as (Dash et al, 2017a)…”

Section: Problem Formulationmentioning

confidence: 99%

“…The SE is defined as the sum of square of errors resulted from actual and estimated parameter after learning which is given as (Dash et al, 2017a)…”

Section: Apd and Se Performancementioning

confidence: 99%

See 2 more Smart Citations

Robust estimation of IIR system's parameter using modified particle swarm optimisation algorithm

Dash¹,

Panigrahi²,

Sharma³

2019

IJCISTUDIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…The parameter-related coefficients in a nonlinear regression analysis model were optimized by combining particle swarm with a genetic phase [32] to reduce the vibrations caused by mine blasting that damages the structures around the blasting area. The derived diffusion-free PSO algorithm was used to estimate the parameters of an infinite impulse response system and improve the energy utilization of an infinite sensor network [33]. Wang [34] used a multiobjective PSO algorithm to solve a path-planning problem of mobile robots in a static rough terrain environment.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

Wang

Deng

et al. 2018

Computational Intelligence and Neuroscience

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In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

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Adaptive recursive system identification using optimally tuned Kalman filter by the metaheuristic algorithm

Janjanam,

Saha,

Kar

et al. 2024

Soft Comput

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Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm

Cited by 11 publications

References 24 publications

Robust estimation of IIR system's parameter using modified particle swarm optimisation algorithm

Robust estimation of IIR system's parameter using modified particle swarm optimisation algorithm

Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

Adaptive recursive system identification using optimally tuned Kalman filter by the metaheuristic algorithm

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