Neural network systems for estimating the initial condition in a heat conduction problem

Abstract

This paper describes a neural network approach to the inverse problem of determining the initial temperature distribution on a slab with adiabatic boundary conditions, from transient temperature distribution, obtained at a given time. Two neural network architectures have been proposed to address the problem: the multilayer perceptron with backpropagation and radial basis functions (RBF), both trained with the whole temperature history mapping. The conducted simulations showed RBF networks present better solutions, faster training, but higher noise sensitiveness, as compared to the multilayer perceptron with backpropagation.