NEURAL NETWORKS FOR COMPUTING IN THE ELASTOPLASTIC ANALYSIS OF STRUCTURES

A neural network model is proposed and studied for the treatment of elastoplastic analysis problems. These problems are formulated as Q.P.P.s with inequality subsidiary conditions. In order to treat these conditions the Hopfield model is appropriately generalized and a neural model is proposed covering the case of inequalities. Finally, the parameter identification problem is formulated as a supervised learning problem Numerical applications close the presentation of the theory and the advantages of the neural network approach are illustrated.