基于置信规则库的海基系统性能退化机理分析与预测

Degradation mechanism analysis and prediction of maritime based system performance using belief rule base

海基系统性能退化机理分析和预测对于提高海基系统的生存能力具有重要意义,但需要考虑不确定条件下的多种类型信息,传统方法在处理海基系统的不确定性时效果欠佳,而置信规则库(BRB)作为证据推理方法中的知识库又无法同时处理参数精度优化和组合爆炸问题.对此,采用BRB参数与结构联合优化方法,建立双层优化的海基系统置信规则库最优决策结构,以AIC(Akaike information criterion)为外层模型优化目标, MSE(Mean square error)为内层模型优化目标,实现同时优化的目的.对比模型输出和实际输出,并采用支持向量机(SVM)进行实验,结果表明,采用具有最优决策结构的海基系统置信规则库建模不仅可以降低模型中规则的数量,也可提高建模精度,验证了所提出方法的有效性.

The degradation mechanism analysis and prediction of the maritime-based system performance is vital for improving the survivability of the maritime-based systems. However, traditional methods can’t deal with uncertainty well since many types of information under uncertainty need to be taken into consideration. As the knowledge base of the evidential reasoning approach, the belief rule base(BRB) can not optimize the modeling complexity and modeling accuracy at the same time. In this study, the structure and parameters of the BRB are jointly optimized to construct a bi-level maritime-based system BRB model. In the bi-level model, the Akaike information criterion(AIC) and mean square error(MSE) are used as the upper-level and lower-level optimization objectives, respectively. By comparing the model outputs with actual outputs, the results of the experiment using the support vector machine(SVM) show that,the optimized BRB can not only reduce the number of rules, but also achieve the highest modeling accuracy, and the effectiveness of the proposed method is verified.