Abstract
Case-based reasoning (CBR) is based on the use of past experience in finding the solution to a new but similar problem. CBR is composed of five main phases: input of the new problem, retrieval of the most similar cases from the knowledge base, adaptation of retrieved cases, validation of the new solution, and learning of the system by adding the approved solution to the knowledge base. Adaptation is the most difficult and the most important step in the successful use of the CBR strategy. Two adaptation methods that are based on fuzzy and rough sets are presented in this paper. The given example illustrates selection of a vibrofluidized bed (VFB) dryer, determination of a drying time, and evaluation of the final moisture content based on application of both adaptation methods.
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