Title: Statistical and incremental methods for neural models selection
Authors: Slim Abid; Mohamed Chtourou; Mohamed Djemel
Addresses: Control and Energy Management Lab (CEM LAB), National School of Engineering of Sfax, University of Sfax, B.P. 1173, 3038 Sfax, Tunisia ' Control and Energy Management Lab (CEM LAB), National School of Engineering of Sfax, University of Sfax, B.P. 1173, 3038 Sfax, Tunisia ' Control and Energy Management Lab (CEM LAB), National School of Engineering of Sfax, University of Sfax, B.P. 1173, 3038 Sfax, Tunisia
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.
Keywords: model selection; neural models; statistical tests; incremental algorithm; artificial intelligence; system identification; dynamic systems.
DOI: 10.1504/IJAISC.2014.059287
International Journal of Artificial Intelligence and Soft Computing, 2014 Vol.4 No.1, pp.41 - 57
Accepted: 24 May 2013
Published online: 28 Jun 2014 *