Applying the Hopfield Neural Network to the Solution of the Temperature Distribution Field in Heat Conduction Problems

Abstract

This article employs the continuous-time analog Hopfield neural network (CHNN) to compute the temperature distribution in one- and two-dimensional transient heat conduction problems. The relationship between the CHNN synaptic connection weights and the governing equations of the problems is established and a corresponding network connectivity circuit design scheme proposed. The CHNN algorithm is initially applied to the solution of conventional problems and is then used to solve more complicated problems involving time-varying heat flux profiles. The results confirm that the CHNN scheme provides an accurate means of solving the transient temperature distributions of heat conduction problems on a real-time basis.