Neural network solution of pantograph type differential equations
Copyright policy )Neural network solution of pantograph type differential equations
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.
- Ural Federal University Russian Federation
- Democritus University of Thrace Greece
- South Ural State University Russian Federation
- Yulin Normal University China (People's Republic of)
- Yulin Normal University China (People's Republic of)
Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations, pantograph equations, Learning and adaptive systems in artificial intelligence, Computational learning theory, Numerical methods for ordinary differential equations, neural networks, approximate solutions
Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations, pantograph equations, Learning and adaptive systems in artificial intelligence, Computational learning theory, Numerical methods for ordinary differential equations, neural networks, approximate solutions
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