Neural network solution of pantograph type differential equations
Chih‐Chun Hou, Theodore E. Simos, Ioannis Th. Famelis
Abstract
We investigate the approximate solution of pantograph type functional differential equations using neural networks. The methodology is based on the ideas of Lagaris et al, and itis applied to various problems with a proportional delay term subject to initial or boundary conditions. The proposed methodology proves to be very efficient.
Topics & Concepts
PantographMathematicsType (biology)Artificial neural networkDifferential equationDelay differential equationTerm (time)Boundary value problemApplied mathematicsMathematical analysisControl theory (sociology)Computer scienceControl (management)Artificial intelligenceEngineeringQuantum mechanicsEcologyPhysicsBiologyMechanical engineeringFractional Differential Equations SolutionsNeural Networks and ApplicationsModel Reduction and Neural Networks