数据驱动的置信规则库构建与推理方法

针对Liu等(LIU J,MARTINEZ L,CALZADA A,et al.A novel belief rule base representation,generation and its inference methodology.Knowledge-Based Systems,2013,53:129-141)提出的扩展置信规则库(BRB)推理精度不够高的问题,提出了一种改进的规则库构建与推理方法。在Liu等提出的规则库构建方法的基础上,给出了一种新的生成规则前件与计算规则权重的方法;同时为了避免大量不必要的规则被激活,引入80/20法则改进规则激活策略,并最终形成完整的置信规则库构建与推理方法。通过输油管道检漏的实例对所提方法的准确性和效率进行对比分析。实验结果表明,所提方法能够在保证低耗时的同时,将系统平均绝对误差(MAE)降低到0.17342,具有较高的效率和精度。

Research on Oil Pipeline Leak Detection and Location system

Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline.

置信规则库参数学习的并行差分进化算法

为解决置信规则库中现有参数学习方法主要是串行算法且不适用于求解大数据下参数优化模型的问题,结合群智能算法中的差分进化算法和集群系统中分布式方法,提出了基于消息传递接口的并行参数学习方法。以输油管道检漏问题为例,对比分析了本算法与现有参数学习方法在收敛时的误差,并在不同结点数的集群系统中分析了本算法的加速比和效率。实验结果表明,并行的参数学习方法是有效可行的。