Torra, Vicenç and Garcia-Alfaro, Joaquin (2019) Towards an Adaptive Defuzzification: Using Numerical Choquet Integral. In: Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science book series (LNCS) (11676). Springer, pp. 113-125. ISBN 9783030267728

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Abstract

Fuzzy systems have been proven to be an effective tool for modeling and control in real applications. Fuzzy control is a well established area that is used in a large number of real systems. Fuzzy rule based systems are defined in terms of rules in which the concepts that define the rules (both in the antecedent and consequent) can be defined in terms of fuzzy sets. In applications, rules are fired and then a set of consequents need to be combined to make a final decision. This final decision is often computed by means of a defuzzification method. In this paper we discuss the defuzzification proces and propose the use of a Choquet integral for this process. In contrast with standard defuzzification methods which are based on mean operators (usually discrete), the Choquet integral permits us to have an output variable with values that have different importances and with interactions among the values themselves. To illustrate the approach, we use a numerical Choquet integral software for continuous functions that we have recently developed. We also position the application of the approach to handle the uncertainty associated to a mission-oriented Cyber-Physical System (CPS).

Item Type:	Book Section
Additional Information:	Cite as: Torra V., Garcia-Alfaro J. (2019) Towards an Adaptive Defuzzification: Using Numerical Choquet Integral. In: Torra V., Narukawa Y., Pasi G., Viviani M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science, vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_11
Keywords:	Adaptive Defuzzification; Numerical; Choquet; Integral;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14379
Identification Number:	https://doi.org/10.1007/978-3-030-26773-5
Depositing User:	Vicenç Torra
Date Deposited:	27 Apr 2021 14:03
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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