6533b822fe1ef96bd127d979
RESEARCH PRODUCT
Neural network approach to solving fuzzy nonlinear equations using Z-numbers
Sina RazvarzRaheleh JafariAlexander Gegovsubject
Property (programming)Mathematics::General MathematicsReliability (computer networking)Structure (category theory)MathematicsofComputing_NUMERICALANALYSIS02 engineering and technologyfuzzy equationFuzzy logicArtificial IntelligenceComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0202 electrical engineering electronic engineering information engineeringApplied mathematics/dk/atira/pure/subjectarea/asjc/1700MathematicsArtificial neural networkZ numberApplied MathematicsComputingSystems modelingNonlinear systemComputational Theory and MathematicsControl and Systems EngineeringUncertain nonlinear systemmultilayer neural network020201 artificial intelligence & image processingComputingMethodologies_GENERAL/dk/atira/pure/core/subjects/computingInterpolationComputer Science(all)description
In this article, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this article, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this article, in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-07-01 |