Neural networks for computing in structural analysis: Methods and prospects of applications

Abstract

Abstract A neural network model is proposed and studied for the treatment of structural analysis problems. Both the cases of bilateral and unilateral constraints are considered and Hopfield‐like neural models are proposed. Moreover, new results, generalizing the results of Hopfield and Tank, 10 are obtained. Numerical applications illustrate the theory and show clearly the advantages of the neural network approach. Finally, the parameter identification problem is formulated and solved as a ‘learning’ problem for a neural network.