Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
An introduction to fuzzy control
An introduction to fuzzy control
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Some properties of defuzzification neural networks
Fuzzy Sets and Systems
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Cooperative neighbors in defuzzification
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Reconstruction problem and information granularity
IEEE Transactions on Fuzzy Systems
A formal approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
A Fuzzy-Logic Mapper for Audiovisual Media
Computer Music Journal
Expert system based controller for the high-accuracy automatic assembly of vehicle headlamps
Expert Systems with Applications: An International Journal
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Defuzzification is an important operation in the theory of fuzzy sets. It transforms a fuzzy set information into a numeric data information. This operation along with the operation of fuzzification is critical to the design of fuzzy systems as both of these operations provide nexus between the fuzzy set domain and the real valued scalar domain. We need the synergy of both of these domains to solve many of our ill-posed problems effectively. In this paper, we will address the problem of defuzzification, present merits and demerits of various defuzzification strategies that are used in the theory and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and fuzzy inference-based systems. We also present in this paper a simple and yet novel defuzzification mechanism.