Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning

See, Hung Lau and Tay, Kai Meng and Chee, Khoon Ng (2013) Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning. Fuzzy Systems (FUZZ), 2013 IEEE International Conference. pp. 1-7. ISSN 1098-7584

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Abstract

Recent research on Single Input Rule Modules (SIRMs)-connected fuzzy inference system (FIS) focuses on its monotonicity property fulfillment. The aim of this paper is to propose an alternative approach for modeling of monotonicity-preserving SIRMs-connected FIS. A new monotonicity index (MI) for approximating the monotonicity property fulfillment of an SIRMs-connected FIS is proposed. A hybrid of Harmony Search (HS), SIRMs-connected FIS, and the new MI is investigated. A proposed data-driven monotonicity-preserving SIRMs-connected FIS model with HS is then presented. The use of MI for tuning of an SIRMs-connected FIS is demonstrated too.

Item Type: Article
Uncontrolled Keywords: unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak, single input rule modules connected fuzzy inference system, harmony search, monotonicity index, data-driven, learning, tuning
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 28 Jul 2015 02:25
Last Modified: 28 Jul 2015 02:25
URI: http://ir.unimas.my/id/eprint/8363

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