Affiliations: [a] Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, Canada | [b] Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia | [c] Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Correspondence:
[*]
Corresponding author: Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada. E-mail:[email protected]
Abstract: In this study, we propose a new concept of granular
rule-based models whose rules assume a format ``if G(Ai) then
G(fi)'' where G$(.)s are granular generalizations of the numeric
conditions and conclusions of the rules. Those generalizations can be
expressed e.g., in terms of interval-valued, type-2 or probabilistic fuzzy
sets. We discuss several classes of fuzzy models depending upon available
information granules and offer a motivation present behind their emergence.
The design of these granular architectures exploits the essentials of
Granular Computing such as a principle of justifiable granularity and an
optimal allocation of information granularity. Detailed investigations of
the performance indexes (objective functions) along with the related
optimization schemes are covered as well.
Keywords: Rule-based models, granular fuzzy models, Granular Computing, optimal allocation of information granularity, principle of justifiable granularity, type-2 fuzzy sets, parameter estimation
