Solving differential equations with unsupervised neural networks

A recent method for solving differential equations using feedforward neural networks was applied to a non-steady fixed bed non-catalytic solid-gas reactor. As neural networks have universal approximation capabilities, it is possible to postulate them as solutions for a given DE problem that defines...

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Detalles Bibliográficos
Autores principales: Parisi, D.R., Mariani, M.C., Laborde, M.A.
Formato: JOUR
Materias:
Neural networks differential equations
Non-catalytic solid-gas reactor simulations
Approximation theory
Computational complexity
Error analysis
Genetic algorithms
Problem solving
Solid-gas reactors
Feedforward neural networks
computer modeling
differential equation
neural network
reactor
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_02552701_v42_n8-9_p715_Parisi
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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