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

被引：36

作者：

Ahmad S.S.S. ^{[1
]}

Pedrycz W. ^{[2
]}

机构：

[1] Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka

[2] Department of Electrical and Computer Engineering, University of Alberta, Edmonton

来源：

Granular Computing | 2017年 / 2卷 / 01期

关键词：

Granular fuzzy sets; Optimal allocation of information granularity; Particle swarm optimization; Rule-based systems; Structural compression;

D O I：

10.1007/s41066-016-0022-5

中图分类号：

学科分类号：

摘要：

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. © 2016, Springer International Publishing Switzerland.

引用

页码：1 / 12

页数：11

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