Neurodynamic Network for Absolute Value Equations: A Fixed-Time Convergence Technique
Xingxing Ju, Chuandong Li, Xin Han, Xing He
Abstract
In this brief, a novel projection neurodynamic network with fixed-time convergence is proposed for solving absolute value equations. In contrast to most existing projection neurodynamic networks, a conservative settling time of the proposed projection neurodynamic network is presented. It is shown that the solution of the proposed approach converges to the solution of the corresponding absolute value equation in fixed-time under some mild conditions. Finally, a numerical example is presented to validate the main results.
Topics & Concepts
Convergence (economics)Projection (relational algebra)MathematicsFixed pointInitial value problemSettling timeApplied mathematicsValue (mathematics)Artificial neural networkContrast (vision)Projection methodMathematical optimizationComputer scienceMathematical analysisAlgorithmDykstra's projection algorithmArtificial intelligenceStatisticsEngineeringEconomic growthEconomicsStep responseControl engineeringNeural Networks and ApplicationsAdaptive Control of Nonlinear SystemsControl Systems and Identification