CAJ | 학술논문

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。
위해결치신규칙고중현유삼수학습방법주요시천행산법차불괄용우구해대수거하삼수우화모형적문제，결합군지능산법중적차분진화산법화집군계통중분포식방법，제출료기우소식전체접구적병행삼수학습방법。이수유관도검루문제위례，대비분석료본산법여현유삼수학습방법재수렴시적오차，병재불동결점수적집군계통중분석료본산법적가속비화효솔。실험결과표명，병행적삼수학습방법시유효가행적。
To solve the problem of the existing parameter learning approaches for Belief Rule Base (BRB)were mainly serial algorithms,and those approaches were unsuitable for handling parameter optimization model under the big data. The differential evolution algorithm of swarm intelligence algorithms and the distributed method of cluster systems were introduced to the BRB,and then a parallel parameter learning approach using message passing interface was proposed. A numeric example of the pipeline leak detection problem was given.The new approach was compared with the existing parameter approaches in terms of the convergence error,the speedup ratio and the efficiency of parallel algorithm with different nodes of the cluster system.The experimental results showed that the approach was feasibilitiness and effective-ness.