Research on Operational Effectiveness Evaluation of Underwater Vehicles Based on Bayesian Optimized BP Neural Network

Pu Shi; Wang Jiarong; Jia Yushan; Fu Xuezhi

doi:10.11993/j.issn.2096-3920.2026-0039

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Pu Shi, Wang Jiarong, Jia Yushan, Fu Xuezhi. Research on Operational Effectiveness Evaluation of Underwater Vehicles Based on Bayesian Optimized BP Neural Network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0039

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Pu Shi, Wang Jiarong, Jia Yushan, Fu Xuezhi. Research on Operational Effectiveness Evaluation of Underwater Vehicles Based on Bayesian Optimized BP Neural Network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0039

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Research on Operational Effectiveness Evaluation of Underwater Vehicles Based on Bayesian Optimized BP Neural Network

doi: 10.11993/j.issn.2096-3920.2026-0039

Unit 92228, Chinese People’s Liberation Army, Beijing, 102400

Received Date: 2026-02-12
Accepted Date: 2026-05-14
Rev Recd Date: 2026-05-13

Available Online: 2026-09-17

Abstract

Abstract

Aiming at the problem of operational effectiveness evaluation of underwater vehicles, an evaluation indicator system for reconnaissance operational effectiveness of underwater vehicles has been established, consisting of 9 core indicators and 6 nonlinear interaction indicators. Traditional BP neural networks are prone to fall into local optima in operational effectiveness evaluation. Therefore, Bayesian Optimization (BO) is used to optimize the hyperparameters of the BP neural network, and an operational effectiveness evaluation model of underwater vehicles based on the BO-BP neural network is established. The simulation verification of the model is realized by Python.The operational effectiveness evaluation indicator system and engineering-oriented adaptation scheme constructed in this paper can more accurately reflect the actual operational effectiveness of underwater vehicles, and provide practically valuable references for their equipment demonstration, operational deployment, modification and upgrading. Compared with generalized evaluation schemes, the proposed approach exhibits significant improvements in scenario adaptability, evaluation accuracy and engineering practical value.
- Underwater Vehicle,
- Bayesian Optimization,
- BP Neural Network,
- Operational Effectiveness Evaluation

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References(19)

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