Constructing membership functions using statistical data
Fuzzy Sets and Systems
Membership function as an evaluation
Fuzzy Sets and Systems
Function approximation with polynomial membership functions and alternating cluster estimation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Construction of differentiable membership functions
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Fuzzy Sets and Systems
Learning fuzzy rules with tabu search-an application to control
IEEE Transactions on Fuzzy Systems
Fuzzy controller design by using neural network techniques
IEEE Transactions on Fuzzy Systems
Computationally efficient reasoning using approximated fuzzy intervals
Fuzzy Sets and Systems
A new type of approximation for fuzzy intervals
Fuzzy Sets and Systems
Rule based fuzzy classification using squashing functions
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Soft Computing and Applications
Fuzzy color-based approach for understanding animated movies content in the indexing task
Journal on Image and Video Processing - Color in Image and Video Processing
On the approximation of compact fuzzy sets
Computers & Mathematics with Applications
On polygonal fuzzy sets and numbers
Fuzzy Sets and Systems
OBIRE: Ontology Based Bibliographic Information Retrieval in P2P Networks
International Journal of Distributed Systems and Technologies
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In this paper we propose an approximation of piecewise linear membership functions with the help of sigmoid functions and certain arithmetic operations. The gradient-based tuning of piecewise linear membership functions can be achieved with the proposed efficient approximation because it has simple continuous derivatives. With this construction we can even approximate the Lukasiewicz operator family which plays an important role in fuzzy logic, first of all from the theoretical point of view, although in practice in optimization and learning it is rarely used because the lack of good analytical properties, e.g. a continuous gradient. The proposed approximation enlarges the applicability of fuzzy methods to the operators and membership functions where the differentiability is desirable.