Coupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification

Xia, Youran; Yi, Wei-Jian; Zhang, Dingye

doi:10.1016/j.engappai.2022.105100

Cited by 11 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of its simple structure, easy implementation, good global search ability, and fast convergence speed, it is widely used in dynamic optimization problems [16] . But the traditional PSO is easy to fall into local optimum [17] , Xia [15] introduces a state-based adaptive velocity limit strategy (SAVL), chaos optimization strategy, adaptive update strategy and mutation strategy to improve the performance of the algorithm.…”

Section: Campso-savlmentioning

confidence: 99%

“…In order to gain the accurate aerodynamic parameters, under the maximum likelihood criterion, the aerodynamic parameter identification problem can be transformed into an optimal estimation problem, and then the parameters of the aerodynamic are optimized using CAMPSO-SAVL [15] to make the deviation minimized between the output of the projectile model and the practical measured data.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Xia,

Yuan,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

Aerodynamic parameters play a key role in determining the trajectory and stability of projectiles. In order to weaken the impact of the projectile movement model, the PSO variant named chaos adaptive mutation PSO with a stated based adaptive velocity limit (CAMPSO-SAVL) is applied to solve the projectile identification equation (based on maximum likelihood criterion), and output the optimal estimation of aerodynamic parameters. Ballistic reconstruction is carried out based on the identification results. The reconstruction results show that the identification results of CAMPSO-SAVL have excellent accuracy and can meet the engineering needs.

show abstract

Section: Campso-savlmentioning

confidence: 99%

mentioning

confidence: 99%

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Xia,

Yuan,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

show abstract

“…It is more suitable for dealing with corrosion prediction scenarios with limited data and high requirements for prediction accuracy [37]. PSO optimizes ELM to establish an external corrosion prediction model for buried pipelines, verified by case analysis on model robustness and superiority [38]. While gray predictive models can be used with limited data, they ignore corrosion-related factors and only model the corrosion prediction process over time.…”

Section: Introductionmentioning

confidence: 99%

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R2), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R2 of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.

show abstract

“…The structural parameters (input weights and hidden thresholds) of ELM are generated randomly and require no iterative adjustment. Owing to it, ELM has low computational complexity and good real-time performance and has been widely used in cloud computing, data visualization, and random projection [40][41][42]. Akusok et al [41] applied ELM to identify the drag coefficient of the projectile for the first time.…”

Section: Introductionmentioning

confidence: 99%

“…Affected by uncertain factors such as the actual combat environment and external meteorological conditions, the ballistic trajectory data is characterized by solid nonlinearity, time-dependent nature, and susceptibility to random noise. Randomly generated structural parameters lead to the oscillation of ELM identification results [42]. In addition, when a single ELM is used to identify the aerodynamic parameters of the projectile, all the given training samples are often used to model the global situation.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Wang

Xia

2023

International Journal of Aerospace Engineering

Self Cite

View full text Add to dashboard Cite

The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.

show abstract

Coupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification

Cited by 11 publications

References 45 publications

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Contact Info

Product

Resources

About