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.