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Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation

Yang Shi, Long Jin, Shuai Li, Jipeng Qiang

2020Journal of the Franklin Institute30 citationsDOIOpen Access PDF

Topics & Concepts

DiscretizationSylvester matrixSylvester equationArtificial neural networkRobustness (evolution)Convergence (economics)Applied mathematicsMatrix (chemical analysis)MathematicsDiscrete time and continuous timeComputer scienceMathematical optimizationArtificial intelligenceMathematical analysisEigenvalues and eigenvectorsPhysicsStatisticsEconomicsEconomic growthComposite materialMatrix polynomialPolynomial matrixQuantum mechanicsPolynomialMaterials scienceGeneBiochemistryChemistryRobotic Mechanisms and DynamicsIterative Learning Control SystemsControl Systems and Identification
Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation | Litcius