Granular Computing as a Basis of System Modelling

摘要

The study is concerned with a linguistic approach to the design of a new category of granular models. In contrast to numerically-driven identification techniques, in granular modeling we concentrate on building meaningful linguistic labels (granules) in the space of experimental data and forming the ensuing model as a web of associations between such granules. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format We refer to them as linguistic (granular) models. Furthermore, as there are no detailed numeric estimation procedures involved in the construction of the linguistic models carried out in this way, their design mode can be viewed as that of a rapid prototyping. The underlying algorithm used in the development of the models utilizes an augmented version of the clustering technique (context-based clustering) that is centered around a notion of linguistic contexts - a collection of fuzzy sets or fuzzy relations defined in the data space (more precisely a space of input variables). The detailed design algorithm is provided and contrasted with the standard modeling approaches commonly encountered in the literature. The usefulness of the linguistic mode of system modeling is discussed and illustrated with the aid of numeric studies. The proposed modelling paradigm is highly user-friendly: it helps the user/modeller to provide with granular data (that are in rapport with the reality of complex systems) and generates modelling outcomes that are also granular and therefore easy to interpret by the end-user. It is also claimed that standard numeric models are subsumed in this category of models as their special case.