Topic
Membership function
About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.
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11 Sep 2006TL;DR: An embedded Real-Time Type-2 Neuro-Fuzzy Controller (RT2NFC) which overcomes the iterative type-reduction overhead and learns the parameters of interval type-2 FLC for marine engines is introduced.
Abstract: Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 Fuzzy Logic Controllers (FLCs) can handle such uncertainties; however they have a computational overhead associated with the iterative type-reduction process which can diminish the FLC real-time performance. Furthermore, manually designing a type-2 FLC is a difficult task particularly as the number of membership function parameters and rules increase. In this paper, we will introduce an embedded Real-Time Type-2 Neuro-Fuzzy Controller (RT2NFC) which overcomes the iterative type-reduction overhead and learns the parameters of interval type-2 FLC for marine engines. We have performed numerous experiments on a real diesel engine testing platform in which we compared our RT2NFC to a T2NFC based on the iterative type reduction procedure. Both T2NFCs were embedded on an industrial microcontroller platform where they handled the uncertainties to produce accurate and robust speed controllers that outperformed the currently used commercial engine controller. The RT2NFC gave approximately the same control response as the T2NFC, whilst the RT2NFC avoided the type-reduction overhead thus giving a faster real-time response.
110 citations
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TL;DR: Using an interval-valued fuzzy framework, this paper presents SAW-based and TOPSIS-based MCDA methods and conducts a comparative study through computational experiments, suggesting that evident similarities exist between the interval- valued fuzzy SAW and TOPsIS rankings.
Abstract: Interval-valued fuzzy sets involve more uncertainties than ordinary fuzzy sets and can be used to capture imprecise or uncertain decision information in fields that require multiple-criteria decision analysis (MCDA). This paper takes the simple additive weighting (SAW) method and the technique for order preference by similarity to an ideal solution (TOPSIS) as the main structure to deal with interval-valued fuzzy evaluation information. Using an interval-valued fuzzy framework, this paper presents SAW-based and TOPSIS-based MCDA methods and conducts a comparative study through computational experiments. Comprehensive discussions have been made on the influence of score functions and weight constraints, where the score function represents an aggregated effect of positive and negative evaluations in performance ratings and the weight constraint consists of the unbiased condition, positivity bias, and negativity bias. The correlations and contradiction rates obtained in the experiments suggest that evident similarities exist between the interval-valued fuzzy SAW and TOPSIS rankings.
110 citations
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TL;DR: A method to rank the fuzzy efficiency scores without knowing the exact form of the membership functions is devised, to apply the maximizing set–minimizing set method, which is normally applied when membership functions are known.
110 citations
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TL;DR: This paper test some widely used fuzzy implication operators with respect to the recently introduced Smets-Magrez axioms and presents a unified generalization of Zadeh's compositional rule of inference.
110 citations
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TL;DR: The presented fuzzy clustering problem uses the distance between observations and location parameter vectors, which is based on the L1-norm, instead of the inner product induced norm used in classical fuzzy ISODATA.
109 citations