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 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.