Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity
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SubjectKnowledge transfer and knowledge reusability; Granular model; Information granularity; Optimal granularity allocation; Software cost estimation; Computational intelligence
In this study, we introduce and discuss a concept of knowledge transfer in system modeling. In a nutshell, knowledge transfer is about ways on how a source of knowledge (that is an existing model) can be used in presence of new, very limited experimental evidence. As such new data are very scarce; they are not sufficient to construct a new model. At the same time, the new data originate from a similar (but not the same) phenomenon (process) for which the original model has been constructed. Such situations can be encountered in software engineering where in spite existing similarities, each project, process, product exhibits its own unique characteristics. Taking this into consideration, the existing model is generalized (abstracted) by forming its granular counterpart- granular model where its parameters are regarded as information granules rather than numeric entities, viz. their non-numeric (granular) version is formed based on the values of the numeric parameters present in the original model. The results produced by the granular model are also granular and in this manner they become reflective of the differences existing between the current phenomenon and the process for which the previous model has been formed. In the study on knowledge transfer and reusability, information granularity is viewed as an important design asset and as such it is subject to optimization. We formulate an optimal allocation problem: assuming a certain level of granularity, distribute it among the parameters of the model (making them granular) so that a certain data coverage criterion is maximized. While the underlying concept is general and applicable to a variety of models, in this study, we discuss its use to fuzzy neural networks. Several granularity allocation protocols are discussed and their effectiveness is quantified. The use of Particle Swarm Optimization (PSO) as the underlying optimization tool to realize optimal granularity allocation is discussed.
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