Structural optimization by gradient-based neural networks

In this paper a neurocomputing strategy is presented which combines data processing capabilities of neural networks and numerical structural optimization. In this strategy, an improved counterpropagation neural network is used. Two arti"cial neural networks are trained, one for the constraints and the other for the gradients of the constraints and structural optimization is accomplished by using these nets. All required parameters such as weight matrices in the neural networks or the gradient computations are automated in this neuro-optimizer strategy. Numerical examples are included to demonstrate the accuracy of the results. Copyright ( 1999 John Wiley & Sons, Ltd