Balanced simplicity–accuracy neural network model families for system identification

Abstract

Nonlinear system identification tends to provide 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 identification 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 Neurocomputing 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 different systems are identified: The first one corresponds to the unavoidable Wiener---Hammerstein system proposed in SYSID2009 as a benchmark; the second system is a flexible robot arm; and the third system corresponds to an acoustic duct.