The Monotonicity And Sub-Additivity Properties Of Fuzzy Inference Systems And Their Applications

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Date
2011-01
Authors
Tay, Kai Meng
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Publisher
Universiti Sains Malaysia
Abstract
The Fuzzy Inference System (FIS) is a popular computing paradigm for undertaking modelling, control, and decision-making problems. In this thesis, the focus of investigation is on two theoretical properties of an FIS model, i.e., the monotonicity and sub-additivity properties. These properties are defined, and their applicability to tackling real-world problems is discussed. This research contributes to formulating a systematic procedure that is based on a mathematical foundation (i.e., the sufficient conditions) to develop monotonicity-preserving FIS models. A method to improve the sub-additivity property is also proposed. The applicability of these proposed approaches are demonstrated using real-world problems, i.e., Failure Mode and Effect Analysis (FMEA) methodology, education assessment problem, and control problem. The use of Fuzzy Rule Interpolation (FRI) for handling the incomplete rule base issue in FIS modelling is studied. This research indicates that whenever the monotonicity property is needed, FRI that predicts each rule consequent separately is not a viable solution to handling the incomplete rule base problem in multi-input FIS-based models. As such, FRI is formulated as a constrained optimization problem for the case of multi-input FIS-based models. A new FRI technique incorporating a Non-linear Programming (NLP) method with the sufficient conditions is proposed, and its application to the FMEA methodology is demonstrated. The results confirm the effectiveness of the new FRI scheme in satisfying the monotonicity property in undertaking FMEA problems.
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Keywords
The monotonicity and sub-additivity Properties , of fuzzy inference systems And their applications
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