Statistical and incremental methods for neural models selection Online publication date: Sat, 28-Jun-2014
by Slim Abid; Mohamed Chtourou; Mohamed Djemel
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 4, No. 1, 2014
Abstract: 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.
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