Statistical and incremental methods for neural models selection

This work presents two methods of selection of neural models for identification of dynamic systems. Initially, a strategy of selection based on statistical tests, which relates to training and generalisation performances of a neural model is analysed. In the second time, a new constructive approach of neural model selection, which the training begins with minimal structure and then incrementally adds new hidden units and/or layers, is described. The simulation and the application of these methods for selection of neural models are also considered.