System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Handbook of Neural Computation
Handbook of Neural Computation
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Theme Editor's Introduction: Neural Networks in Computational Science and Engineering
IEEE Computational Science & Engineering
Identification of Dynamical Systems
Identification of Dynamical Systems
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
A differential evolution based neural network approach to nonlinear system identification
Applied Soft Computing
Expert Systems with Applications: An International Journal
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural Computing and Applications - Special Issue on LSMS2010 and ICSEE 2010
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In this paper a dedicated recurrent neural network design and a model reduction approach are proposed in order to improve the balance between complexity and quality of black box nonlinear system identification models. The proposed neural network design, based on a three-layers architecture, helps to reduce the number of parameters of the model after the training phase without any significant loss of estimation accuracy. Nevertheless, the proposed architecture remains sufficiently general to provide a wide range of models among the most encountered in the literature. This reduction, achieved by a convenient choice of the activation functions and the initial conditions of the synaptic weights, is developed in two steps. The first step is to train the proposed architecture under two reasonable assumptions. Then the recurrent three-layers neural network is transformed into a representation of two-layer with less number of neurons, that is, a significant reduced number of parameters. The constructed architecture provided models with reasonable reduced number of parameters with a convenient estimation accuracy. To validate the proposed approach, we identify the Wiener-Hammerstein benchmark nonlinear system proposed in SYSID2009 [1].