Type-1 and Interval Type-2 Monotone Takagi-Sugeno-Kang Fuzzy Inference Systems and Their Practical Application
This research focuses on the analysis and application of Type-1 (T1) and Interval Type-2 (IT2) Fuzzy Inference Systems (FIS) to ensure monotonicity, a fundamental concept delineating functions with consistent behavior. Even though mathematical conditions to ensure the monotonicity property of T1 and...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/46169/3/Type-1%20and%20Interval%20Type-2%20Monotone%20Takagi-Sugeno-Kang%20Fuzzy%20Inference%20Systems%20and%20Their%20Practical%20Application%20%5BSigned%5D.pdf |
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| Summary: | This research focuses on the analysis and application of Type-1 (T1) and Interval Type-2 (IT2) Fuzzy Inference Systems (FIS) to ensure monotonicity, a fundamental concept delineating functions with consistent behavior. Even though mathematical conditions to ensure the monotonicity property of T1 and IT2 FIS are available, it is not clear how to design the fuzzy membership functions (FMFs) at the antecedent part of a fuzzy rule base such that the resulting FIS model is always monotone. This research addresses the uncertainties and inconsistencies in fuzzy rules affecting the monotonicity of FIS outputs.
The study reviews sufficient conditions for T1 and IT2 FIS to satisfy monotonicity, analyzing T1 FIS through simulation experiments and exploring its applications in image processing (edge detection) and COVID-19 mitigation strategies. IT2 FIS is similarly reviewed and analyzed. The research investigates a Transmission Cause and Effect Analysis (TCEA) procedure using monotone Interval Type-2 Takagi-Sugeno-Kang (TSK) FIS and tests its applicability in image processing. MATLAB is used for simulation experiments,
measuring effectiveness through performance metrics specific to image processing and COVID-19 mitigation. This multidisciplinary approach combines theoretical analysis with practical applications, contributing to the understanding of how fuzzy logic can be leveraged in various domains. The findings are expected to benefit researchers and practitioners addressing complex problems and underline the potential of FIS in nonlinear systems modelling. |
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