<title>Granular computing for system modeling</title>

The study is concerned with the fundamentals of granular computing and its use to system modeling and system simulation. In contrast to numerically-driven identification techniques, in granular modeling we concentrate on building meaningful information granules in the space of experimental data and forming the ensuing model as a web of associations between such constructs. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format. First, we elaborate on the role of information granules viewed as basic building modules exploited in model development. Second, we show how information granules are constructed. It is shown how to express relationships (links) between information granules; in this case two measures of linkage are discussed, namely a relevance index and a notion of a fuzzy correlation. Granular computing invokes a number of layers whose existence is implied by different levels of information granularity. We show how to move between these layers by using transformations of encoding and decoding of information granules. Subsequently, some generic architectures of granular modeling are discussed.