Surrogate solutions of Fredholm equations by feedforward networks

Accuracy of Surrogate Solutions of Integral Equations by Feedforward Networks

Surrogate solutions of Fredholm integral equations by feedforward neural networks are investigated theoretically. Convergence of surrogate solutions computable by networks with increasing numbers of computational units to theoretically optimal solutions is proven and upper bounds on rates of convergence are derived. The results hold for a variety of computational units, they are illustrated by ...

Surrogate Modelling of Solutions of Integral Equations by Neural Networks

Surrogate modelling of solutions of integral equations by neural networks is investigated theoretically. Estimates of speed of convergence of suboptimal surrogate solutions to solutions described by Fredholm theorem are derived.

Approximate Analytical Solutions of Fuzzy Linear Fredholm Integral Equations by HAM

Numerical solution of fuzzy linear Fredholm integro-differential equation by \fuzzy neural network

In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from ...

A computational wavelet method for numerical solution of stochastic Volterra-Fredholm integral equations

A Legendre wavelet method is presented for numerical solutions of stochastic Volterra-Fredholm integral equations. The main characteristic of the proposed method is that it reduces stochastic Volterra-Fredholm integral equations into a linear system of equations. Convergence and error analysis of the Legendre wavelets basis are investigated. The efficiency and accuracy of the proposed method wa...