Hazard evaluation of goaf based on DBO algorithm coupled with BP neural network

Wang, Wentong; Zhang, Qianjun; Guo, Sha; Li, Zhixing; Li, Zhiguo; Liu, Chuanju

doi:10.1016/j.heliyon.2024.e34141

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e34141

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Hazard evaluation of goaf based on DBO algorithm coupled with BP neural network

Wentong Wang,

Qianjun Zhang,

Sha Guo

et al.

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2024

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Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

Mei,

Li,

Liu

et al. 2024

JMSE

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As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising.

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Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

Mei,

Li,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

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Hazard evaluation of goaf based on DBO algorithm coupled with BP neural network

Cited by 1 publication

References 11 publications

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

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