Hierarchical modeling of sound velocity field based on improved BPNN

doi:10.11947/j.AGCS.2024.20230035

Abstract

Abstract:

Aiming at the limitations of BPNN in the application of 3D sound velocity field modeling, such as low prediction accuracy, easy to fall into the local optimum, and weak interpretability, a method of the thermohaline field modeling based on BPNNs is proposed, and a sound velocity hierarchical modeling scheme is designed jointly with the empirical equation of sound velocity. Meanwhile, the BPNN function and architecture are improved and optimized by introducing an adaptive particle swarm optimization algorithm to improve the accuracy of the thermohaline field modeling. Experiments on the modeling performance of the proposed algorithm are conducted using BOA_Argo grid data in the central region of the South China Sea. The results show that the algorithm proposed in this paper can fully reflect the physical properties of the ocean sound velocity field, with higher modeling accuracy and robustness than traditional algorithms, and with excellent stability and reliability.

Key words: 3D sound velocity field, back propagation neural network, particle swarm optimization algorithm, BOA_Argo grid data

CLC Number:

P229

Zhaoying WANG, Hongzhou CHAI, Zhenqiang DU. Hierarchical modeling of sound velocity field based on improved BPNN[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2316-2327.

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算法类型	声速误差/（m²/s²）
EOF	0.461 016
IBPNN_c	0.251 132
IBPNN	0.032 693
PSO-IBPNN	0.022 245
APSO-IBPN N	0.024 198

算法类型	建模时间/s
EOF	0.218
IBPNN_c	11
IBPNN	19
PSO-IBPNN	4786
APSO-IBPNN	2458

算法简称	No.1	No.12	No.23	No.43	No.48
PSO-IBPNN	0.258 734	0.044 405	0.213 312	0.035 571	0.061 199
APSO-IBPNN	0.064 499	0.052 736	0.114 254	0.043 104	0.064 571

算法类型	预测耗时
EOF	0.005 8
IBPNN_c	0.000 1
IBPNN	0.266 6
PSO-IBPN N	0.173 0
APSO-IBPNN	0.250 6