基于扩展置信规则库的海盗袭击事件风险预测

为有效应对海盗袭击事件，减少可能产生的损失，本文提出一种新的基于扩展置信规则库 (EBRB)联合优化的海盗袭击事件风险预测模型。通过引入Relief F算法和差分进化算法，从结构和参数两个角度对EBRB系统进行优化，以确保EBRB系统具有最优的参数数量和取值，利用实际海盗事件数据集进行模型验证。结果显示，联合优化的EBRB系统预测结果与实际情况的拟合效果较好，相对于初始的EBRB系统，联合优化EBRB系统将海盗事件的风险预测准确性提高 了60%。此外，与现有其他预测模型对比发现，基于联合优化EBRB系统的预测模型在提高预测准确性方面具有一定的优势。

Extended Belief Rule Based System for Risk Prediction of Pirate Attacks

To effectively predict pirate attacks and reduce possible losses, this paper proposes a pirate attacks risk prediction model based on the joint optimization of extended belief rule base (EBRB). By introducing the Relief F algorithm and differential evolution algorithm, this study optimizes the EBRB system from the perspective of structure and parameters, which ensures the EBRB system has the optimal number and value of parameters. The real pirate event data set is used for the case study. The results show that the reasoning results of the jointly optimized EBRB system match well with the actual situation. Compared with the initial EBRB system, the proposed method improves the risk prediction accuracy of pirate events by 60% . In addition, compared with the traditional prediction models, the prediction model based on the joint optimization of EBRB shows certain advantages in improving the prediction accuracy.