CAJ | 학술논문

建立一个完整的案例推理系统,提出一种高效的案例检索方法和一种案例调整策略.在案例检索过程中,提出一种基于贝叶斯粗糙集的属性权重确定算法,在此基础上利用最邻近法检索出与当前案例最相似的一组案例作为参考.使用检索出的相似案例训练分层混合专家模型,并用微粒群算法优化模型参数,实现了对案例的调整.采用实际转炉生产数据进行仿真,结果表明了该案例推理系统的有效性.
건립일개완정적안례추리계통,제출일충고효적안례검색방법화일충안례조정책략.재안례검색과정중,제출일충기우패협사조조집적속성권중학정산법,재차기출상이용최린근법검색출여당전안례최상사적일조안례작위삼고.사용검색출적상사안례훈련분층혼합전가모형,병용미립군산법우화모형삼수,실현료대안례적조정.채용실제전로생산수거진행방진,결과표명료해안례추리계통적유효성.
An integrated case-based reasoning system is built, and an efficient case retrieval method and a case adjust method are proposed in the system. In the case retrieval process, an algorithm to compute attributes weight is presented based on improved Bayesian rough set model. Then, the most nearest neighbor method is used to retrieve a group of similar cases as the reference of current furnace. In case adjust process, a hierarchical mixture of experts model is trained with this group of similar cases, then particle swarm optimization algorithm is adopted to optimize the parameters. A simulation experiment is implement with practical data from a steel plant. The results show the effectiveness of the proposed case-based reasoning system.