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|Title:||Design of type-reduction strategies for type-2 fuzzy logic systems using genetic algorithms|
|Authors:||Tan, W.-W. |
|Source:||Tan, W.-W.,Wu, D. (2007). Design of type-reduction strategies for type-2 fuzzy logic systems using genetic algorithms. Studies in Computational Intelligence 66 : 169-187. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-72377-6_7|
|Abstract:||Increasingly, research in the field of fuzzy theory is focusing on fuzzy sets (FSs) whose membership functions are themselves fuzzy. The key concept of such type-2 FSs is the footprint of uncertainty. It provides an extra mathematical dimension that equips type-2 fuzzy logic systems (FLSs) with the potential to outperform conventional (type-1) FLSs. While a type-2 FLS has the capability to model more complex relationships, the output of a type-2 fuzzy inference engine is a type-2 FS that needs to be type-reduced before defuzzification can be performed. Unfortunately, type-reduction is usually achieved using the computationally intensive Karnik-Mendel iterative algorithm. In order for type-2 FLSs to be useful for real-time applications, the computational burden of type-reduction needs to be relieved. This work aims at designing computationally efficient type-reducers using a genetic algorithm (GA). The proposed type-reducer is based on the concept known as equivalent type-1 FSs (ET1FSs), a collection of type-1 FSs that replicates the input-output relationship of a type-2 FLS. By replacing a type-2 FS with a collection of ET1FSs, the type-reduction process then simplifies to deciding which ET1FS to employ in a particular situation. The strategy for selecting the ET1FS is evolved by a GA. Results are presented to demonstrate that the proposed type-reducing algorithm has lower computational cost and may provide better performance than FLSs that employ existing type-reducers. © 2007 Springer-Verlag Berlin Heidelberg.|
|Source Title:||Studies in Computational Intelligence|
|Appears in Collections:||Staff Publications|
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