Fuzzy logic is a powerful modelling approach to build control applications and to generate knowledge-based evaluation indices. In both cases, however, the applicability to complex systems is limited by the effort required to formulate the rules, whose number grows rapidly with the number of input variables and membership functions. This work presents a framework that implements the F-IND fuzzy model to simplify the formulation of fuzzy indices, where the rules are automatically generated on the basis of the specification of best and worst cases on the membership functions of each input variable. The paper discusses the method and presents the organization of the Framework that allows automatic code generation, targeting the efficient execution of the calculations on an embedded system. The framework has been tested and validated on real hardware.

A framework for automatic generation of fuzzy evaluation systems for embedded applications

DE MARTINI, DANIELE
Writing – Original Draft Preparation
;
ROVEDA, GIANLUCA
Membro del Collaboration Group
;
Marchini, Agnese
Membro del Collaboration Group
;
Facchinetti, Tullio
Supervision
2017-01-01

Abstract

Fuzzy logic is a powerful modelling approach to build control applications and to generate knowledge-based evaluation indices. In both cases, however, the applicability to complex systems is limited by the effort required to formulate the rules, whose number grows rapidly with the number of input variables and membership functions. This work presents a framework that implements the F-IND fuzzy model to simplify the formulation of fuzzy indices, where the rules are automatically generated on the basis of the specification of best and worst cases on the membership functions of each input variable. The paper discusses the method and presents the organization of the Framework that allows automatic code generation, targeting the efficient execution of the calculations on an embedded system. The framework has been tested and validated on real hardware.
2017
978-989-758-274-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1251746
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