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Balanced simplicity–accuracy neural network model families for system identification

Abstract : Nonlinear system identification tends to pro- vide highly accurate models these last decades; however, the user remains interested in finding a good balance between high-accuracy models and moderate complexity. In this paper, four balanced accuracy–complexity identifi- cation model families are proposed. These models are derived, by selecting different combinations of activation functions in a dedicated neural network design presented in our previous work (Romero-Ugalde et al. in Neurocom- puting 101:170–180. doi:10.1016/j.neucom.2012.08.013, 2013). The neural network, based on a recurrent three-layer architecture, helps to reduce the number of parameters of the model after the training phase without any loss of estimation accuracy. Even if this reduction is achieved by a convenient choice of the activation functions and the initial conditions of the synaptic weights, it nevertheless leads to a wide range of models among the most encountered in the literature. To validate the proposed approach, three dif- ferent systems are identified: The first one corresponds to the unavoidable Wiener–Hammerstein system proposed in SYSID2009 as a benchmark; the second system is a flex- ible robot arm; and the third system corresponds to an acoustic duct.
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Submitted on : Thursday, February 26, 2015 - 3:27:36 PM
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Hector M. Romero Ugalde, Jean-Claude Carmona, Juan Reyes-Reyes, Victor M. Alvarado, Christophe Corbier. Balanced simplicity–accuracy neural network model families for system identification. Neural Computing and Applications, Springer Verlag, 2015, 26 (1), pp.171-186. ⟨10.1007/s00521-014-1716-8⟩. ⟨hal-01120786⟩



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