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Granular Computing

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23-06-2016 | Original Paper

The development of granular rule-based systems: a study in structural model compression

Authors: Sharifah Sakinah Syed Ahmad, Witold Pedrycz

Published in: Granular Computing | Issue 1/2017

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Abstract

In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These constructs arise as a result of a structural compression of fuzzy rule-based systems in which a subset of originally existing rules is retained. Because of the reduced subset of the originally existing rules, the remaining rules are made more abstract (general) by expressing their conditions in the form of granular fuzzy sets (such as interval-valued fuzzy sets, rough fuzzy sets, probabilistic fuzzy sets, etc.), hence the name of granular fuzzy rule-based systems emerging during the compression of the rule bases. The design of these systems dwells upon an important mechanism of allocation of information granularity using which the granular fuzzy rules are formed. The underlying optimization consists of two phases: structural (being of combinatorial character in which a subset of rules is selected) and parametric (when the conditions of the selected rules are made granular through an optimal allocation of information granularity). We implement the cooperative particle swarm optimization to solve optimization problem. A number of experimental studies are reported; those include fuzzy rule-based systems.

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Title: The development of granular rule-based systems: a study in structural model compression
Authors: Sharifah Sakinah Syed Ahmad
Witold Pedrycz
Publication date: 23-06-2016
Publisher: Springer International Publishing
Published in: Granular Computing / Issue 1/2017
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-016-0022-5

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