Results 171 to 180 of about 7,859 (213)

On Quality Defuzzification

1995
We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Furthermore, we give an alternative approach for the case where the output fuzzys sets have different shapes. Finally, we give an example.
H. Hellendoorn, C. Thomas
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Voltage-mode defuzzification circuits

International Journal of Electronics, 1994
The defuzzification interface is an important building block of a fuzzy logic controller (FLC). In this paper we describe two major methods for defuzzification and suitable analogue implementations for voltage-mode operation. A full rail-to-rail voliage range can be obiained using a fixed gain amplifier. SPICE simulations are presenied for each circuit.
ION E. OPRlS, GREGORY T. A. KOVACS
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Fuzzy tests – defuzzification and randomization

Fuzzy Sets and Systems, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Influence value defuzzification method

Proceedings of IEEE 5th International Fuzzy Systems, 2002
This paper describes a computationally efficient method of defuzzification which we call the influence value (IV) algorithm. The algorithm is meant to be used in conjunction with standard max-min (Mamdani) inference and significantly mitigates the computational cost associated with the aggregation operation followed by the determination of the center ...
D.P. Madau, L.A. Feldkamp
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Triangular dense fuzzy sets and new defuzzification methods

Journal of Intelligent & Fuzzy Systems, 2016
This article deals with a new novel defuzzification method for the dense fuzzy sets. In our study, we first define the dense fuzzy set for triangular fuzzy numbers.
S. De, Ismat Beg
semanticscholar   +1 more source

Partition validity and defuzzification

Fuzzy Sets and Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Flores-Sintas, Antonio, Cadenas, José M., Martin, Fernando   +2 more
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Empirical study of defuzzification

22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003, 2004
The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge.
S.S. Lancaster, M.J. Wierman
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Defuzzification: criteria and classification

Fuzzy Sets and Systems, 1999
The paper is devoted to the evaluation of various defuzzification methods encountered in the existing literature. By defuzzification we mean a transformation of a fuzzy set into its numeric representative. General evaluation criteria are outlined (including core selection, scale invariance, monotony, triangular conorm criterion, continuity as well as ...
Van Leekwijck, Werner, Kerre, Etienne E.
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Advanced Inference Filter Defuzzification

2001
In the field of modeling, fuzzy models are one efficient approach for representing technical systems or human control strategies. Fuzzy models have the advantage of supplying a transparent and interpretable model. Conventional fuzzy models are based on fuzzification, inference and defuzzification.
H. Kiendl, P. Krause
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Kernel Based Defuzzification

2013
Defuzzification converts a fuzzy set to a crisp value or set. Standard defuzzification methods are the center of gravity (COG) and the mean of maxima (MOM). A popular parametric defuzzification method is based on the basic defuzzification distribution (BADD).
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