On adaptively trained neural networks
Carlos Eduardo Pedreira, N.M. Roehl · 2005
In this paper a new procedure to adaptively adjust weights in a layered neural network is proposed. Nonlinear programming techniques are used in order to properly calculate the new weight set. This methodology can be used for time varying models with no necessity of retraining One of the main features of our approach concerns the designer flexibility to control a trade off problem between fitting new incoming data and causing minimum damage to the information related to the original data set. We analyze the solution existence.