Abstract
In this study, we discuss the concept of knowledge reuse in Computational Intelligence (CI) by showing how the CI constructs could be formed by reconciling data and a suite of existing models. As the models are reflective of the available knowledge being formed on a basis of existing data, their use is effectively a sound mechanism of knowledge reuse. We introduce a certain performance index whose minimization helps establish the most effective level of knowledge reuse (viz. a level of reliance on the existing models). Fuzzy rule-based systems are used as a comprehensive example which illustrates the algorithmic details of the proposed approach. Furthermore we show, by making use of the principle of justifiable granularity, how the parameters of the rules can be represented in the form of information granules, and triangular fuzzy numbers in particular.