Hybrid Recurrent Neural Network for Nonlinear Hybrid Dynamical Systems Identification
Juan Eduardo Velázquez‐Velázquez, Rosalba Galván‐Guerra, Ieroham Solomon Baruch · 2011
This paper is devoted to the development of a Neural Network Hybrid Identification Framework for unknown Nonlinear Hybrid Dynamical Systems. The proposal is based in the well known Recurrent Trainable Neural Networks Identifiers. In a first instance, the unknown hybrid system is considered like a black-box where by using only hybrid input-output data an approximated model is found. In a second instance, by considering that the hybrid output of the unknown hybrid system is triggered by a defined set of hypersurfaces we extent the approach identification by introducing a Hybrid Recurrent Trainable Neural Network Identifier. The effectiveness of the proposed approach is shown using a commutable pendulum example.