Toward the use of set-membership identification in efficient training of feedforward neural networks

The application of the theory of set-membership identification to the development of efficient learning algorithms for neural networks is discussed. Some results relevant to the application of the method to nonlinear feedforward networks are presented. The techniques discussed have the potential to significantly improve the efficiency of teaching neural networks by employing novel data selection criteria based on set-theoretic constraints.<>