SYSTEM MODELING WITH FUZZY MODELS: FUNDAMENTAL DEVELOPMENTS AND PERSPECTIVES

Document Type: Research Paper

Author

DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING UNIVERSITY OF ALBERTA EDMONTON T6R 2V4 AB CANADA, DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING FACULTY OF ENGINEERING KING ABDULAZIZ UNIVERSITY JEDDAH, 21589 SAUDI ARABIA AND SYSTEMS RESEARCH INSTITUTE POLISH ACADEMY OF SCIENCES, WARSAW POLAND.

Abstract

In this study, we offer a general view at the area of fuzzy modeling and fuzzy
models, identify the visible development phases and elaborate on a new and promising
directions of system modeling by introducing a concept of granular models. Granular
models, especially granular fuzzy models constitute an important generalization of existing
fuzzy models and, in contrast to the existing models, generate results in the form of
information granules (such as intervals, fuzzy sets, rough sets and others). We present a
rationale and deliver some key motivating arguments behind the emergence of granular
models and discuss their underlying design process. Central to the development of granular
models are granular spaces, namely a granular space of parameters of the models and a
granular input space. The development of the granular model is completed through an
optimal allocation of information granularity, which optimizes criteria of coverage and
specificity of granular information. The emergence of granular models of type-2 and type-n,
in general, is discussed along with an elaboration on their formation. It is shown that
achieving a sound coverage-specificity tradeoff (compromise) is of paramount relevance in
the realization of the granular models.

Keywords


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