Varying‐parameter finite‐time zeroing neural network for solving linear algebraic systems
Dimitrios Gerontitis, Lazaros Moysis, Predrag S. Stanimirović, Vasilios N. Katsikis, Christos Volos
Abstract
A new recurrent neural network is presented for solving linear algebraic systems with finite‐time convergence. The proposed model includes an exponential term in the Zhang neural network dynamical system, which leads to a faster convergence of the error‐monitoring function in comparison to previous methods. Theoretical analysis, as well as simulation results, validate the efficacy of the proposed model.
Topics & Concepts
Artificial neural networkConvergence (economics)Algebraic numberApplied mathematicsTerm (time)Computer scienceLinear systemExponential functionFunction (biology)MathematicsControl theory (sociology)AlgorithmArtificial intelligenceMathematical analysisEconomic growthControl (management)BiologyEconomicsEvolutionary biologyPhysicsQuantum mechanicsNeural Networks and ApplicationsRobotic Mechanisms and DynamicsAdvanced Numerical Analysis Techniques