TAO-robust backpropagation learning algorithm

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model -estimates [introduced by Tabatabai, M. A., Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example. © 2005 Elsevier Ltd. All rights reserved

Combining genetic algorithms and the finite element method to improve steel industrial processes

Most of the times the optimal control of steel industrial processes is a very complicated task because of the elevated number of parameters to adjust. For that reason, in steel plants, engineers must estimate the best values of the operational parameters of processes, and sometimes, it is also necessary to obtain the appropriate model for steel material behaviour. This article deals with three successful experiences gained from genetic algorithms and the finite element method in order to solve engineering optimisation problems. On one hand, a fully automated method for determining the best material behaviour laws is described, and on the other hand we present a common methodology to find the most appropriate settings for two cases of improvement in steel industrial processes. The study of the three reported cases allowed us to show the reliability and effectiveness of combining both techniques. © 2012 Elsevier B.V