2016
DOI: 10.1016/j.ijleo.2016.05.145
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Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

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Cited by 35 publications
(20 citation statements)
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References 23 publications
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“…Liu et al [4] established a multihierarchical integrated product design data model supporting the multidisciplinary design optimization (MDO) in the Web environment and a Web services-based framework considering uncertainties was proposed. Zheng and Liao [5] improved particle swarm algorithms, which could be applied to many other parameter identification and optimization problems. Zhang et al [6] presented a modified multiobjective evolutionary algorithm based on the decomposition approach to solve an optimal power flow problem with multiple and competing objectives.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Liu et al [4] established a multihierarchical integrated product design data model supporting the multidisciplinary design optimization (MDO) in the Web environment and a Web services-based framework considering uncertainties was proposed. Zheng and Liao [5] improved particle swarm algorithms, which could be applied to many other parameter identification and optimization problems. Zhang et al [6] presented a modified multiobjective evolutionary algorithm based on the decomposition approach to solve an optimal power flow problem with multiple and competing objectives.…”
Section: Related Workmentioning
confidence: 99%
“…Cai and Aref [34] developed a genetic algorithm-(GA-) based optimization procedure. Zheng and Liao [5] realized parameter identification of nonlinear dynamic systems using an improved particle swarm optimization. Qin et al [35] utilized the differential evolution algorithm (DE) for global numerical optimization.…”
Section: Setting Up the Optimizationmentioning
confidence: 99%
“…The particle swarm optimization algorithm is an evolutionary computing technique which is based on the simulation of birds' flock [23,24]. The basic idea of particle swarm optimization algorithm is to find the optimal solution through collaboration and information sharing among individuals in the group [25].…”
Section: Introductionmentioning
confidence: 99%
“…Update the position of each particle θ i k + 1 by(24). Compute the best position of each particle θ ih k + 1 by(33) (6).…”
mentioning
confidence: 99%
“…For example, particle swarm optimization (PSO) algorithm is used to identify the model parameters in biomedical 13 and environmental applications. 14,15 Another commonly used algorithm is the ant colony optimization, which is used in estimating the model parameter 16,17 for mechanical systems. Bat algorithm, a swarm-based algorithm is also used for parameter estimation.…”
Section: Introductionmentioning
confidence: 99%