Research on Mission Effectiveness Evaluation of Undersea 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

Volume 34 Issue 5

Oct 2026

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PU Shi, WANG Jiarong, JIA Yushan, FU Xuezhi. Research on Mission Effectiveness Evaluation of Undersea Vehicles Based on Bayesian-Optimized BP Neural Network[J]. Journal of Unmanned Undersea Systems, 2026, 34(5): 979-985. doi: 10.11993/j.issn.2096-3920.2026-0039

Citation:

PU Shi, WANG Jiarong, JIA Yushan, FU Xuezhi. Research on Mission Effectiveness Evaluation of Undersea Vehicles Based on Bayesian-Optimized BP Neural Network[J]. Journal of Unmanned Undersea Systems, 2026, 34(5): 979-985. doi: 10.11993/j.issn.2096-3920.2026-0039

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Research on Mission Effectiveness Evaluation of Undersea Vehicles Based on Bayesian-Optimized BP Neural Network

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

Unit 92228^th, Chinese People’s Liberation Army, Beijing 102400, China

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

Available Online: 2026-09-17

Abstract

Abstract

To address the evaluation of the mission effectiveness of undersea vehicles, an evaluation indicator system for their reconnaissance mission effectiveness was established, consisting of 9 core indicators and 6 nonlinear interaction indicators. Traditional backpropagation(BP) neural networks are prone to becoming trapped in local optima when used to evaluate mission effectiveness. Therefore, Bayesian optimization(BO) was used to optimize the hyperparameters of the BP neural network, and an mission effectiveness evaluation model based on a BO-BP neural network was established. The model was implemented and validated using Python. The simulation results show that the mission effectiveness evaluation indicator system and adaptation scheme for engineering applications developed in this paper can more accurately reflect the actual mission effectiveness of undersea vehicles and provide valuable practical guidance for equipment demonstration, mission deployment, modification, and upgrading. Compared with general evaluation schemes, the proposed method offers significant improvements in scenario adaptability, evaluation accuracy, and practical applicability.
- undersea vehicle,
- Bayesian optimization,
- BP neural network,
- mission effectiveness evaluation

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References(19)

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