Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Gao, Zhanpeng; Yi, Wenjun

doi:10.1016/j.measurement.2024.115473

Measurement

2025

DOI: 10.1016/j.measurement.2024.115473

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Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Zhanpeng Gao,

Wenjun Yi

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Cited by 2 publications

References 38 publications

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Cross-domain fault diagnosis using convolutional attention network with an improved dung beetle optimization algorithm

Li,

Luo,

Peng

et al. 2024

Meas. Sci. Technol.

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Rotating machinery plays a critical role in large-scale equipment, and its operational condition significantly influences the stability and safety of the equipment. Therefore, it is imperative to improve the accuracy of fault diagnosis. While deep learning has been widely utilized for fault diagnosis, the effectiveness of the model heavily relies on hyperparameter configuration. Current deep learning methods often necessitate human intervention to fine-tune these hyperparameters, leading to a time-consuming and potentially subjective process. Furthermore, although various meta-heuristic algorithms have been employed for optimizing hyperparameters, these methods are computationally intensive and susceptible to converging on local optimal solutions when dealing with high-dimensional non-convex hyperparameter spaces. To tackle this issue, this paper proposes a cross-domain fault diagnosis using convolutional attention network (CAN) with an improved dung beetle optimization (IDBO) algorithm, called IDBO-CAN algorithm. Firstly, an IDBO algorithm is designed, which mainly uses chaotic local search, levy flight strategy and adaptive lognormal distribution variation to enhance the global optimization capability of the dung beetle optimization algorithm. Secondly, the setting of hyperparameters significantly affects the performance of the CAN using a one-dimensional convolutional neural network. The IDBO algorithm is employed to automatically determine better hyperparameters for CAN. Finally, the performance of IDBO and IDBO-CAN algorithms are evaluated by 13 benchmark functions and multi-source datasets. The experimental results show that IDBO and IDBO-CAN algorithms have excellent performance on many benchmark functions and datasets.

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Cross-domain fault diagnosis using convolutional attention network with an improved dung beetle optimization algorithm

Li,

Luo,

Peng

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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Ballistic Fitting Impact Point Prediction Based on Improved Crayfish Optimization Algorithm

Yang,

Wang,

2024

Aerospace

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To solve the problem of difficulty in predicting the impact point clearly and promptly during projectile flight, this paper proposes an improved ballistic-impact-point prediction method. A certain type of high-spinning tailed projectile is taken as the research object for online real-time landing point prediction research. This study comprehensively utilizes the real-time radar measurement data and the geomagnetic data measured by the bomb-carried geomagnetic sensor. It applies the four-degree-of-freedom ballistic model to predict the landing point. First, the roll angular velocity is calculated based on the geomagnetic data, after which the radar real-time measurement data are segmentally fitted using the improved crayfish algorithm. Then, the fitted parameters are substituted into the four-degree-of-freedom ballistic model. Finally, the C-K method is used to identify the aerodynamic parameters, and the identified aerodynamic parameters are used for fallout prediction. The simulation results show a small deviation between the predicted and actual impact points using the improved ballistic-impact-point prediction method.

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Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Cited by 2 publications

References 38 publications

Cross-domain fault diagnosis using convolutional attention network with an improved dung beetle optimization algorithm

Cross-domain fault diagnosis using convolutional attention network with an improved dung beetle optimization algorithm

Ballistic Fitting Impact Point Prediction Based on Improved Crayfish Optimization Algorithm

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