Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning
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 app...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2013
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/8363/ http://ir.unimas.my/id/eprint/8363/1/Monotonicity%20Preserving%20SIRMs-Connected%20Fuzzy%20%28abstract%29.pdf |
| Summary: | 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. |
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