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Prediction of the Nonlinear Structural Behaviour by Digital Recurrent Neural Network_1

Research Area: Uncategorized Year: 2011
Type of Publication: In Proceedings Keywords: identification, structural behaviour, digital recurrent network
  • Rankovic, Vesna
  • Grujovic, Nenad
  • Divac, Dejan
  • Milivojevic, Nikola
  • Papanikolopoulos, K.
  • Borota, Jelena
Editor: M. Trajanović
Book title: Proceedings of the 34th International Conference on Production Engineering
Pages: 403-406
Organization: University of Niš, Faculty of Mechanical Engineering Month: September
ISBN: 978-86-6055-019-6
The dynamical systems contain nonlinear relations which are difficult to model with conventional techniques. Nonlinear models are needed for system analysis, optimization, simulation and diagnosis of nonlinear systems. In recent years, computational-intelligence techniques such as neural networks, fuzzy logic and combined neuro-fuzzy systems algorithms have become very effective tools to identification nonlinear plants. The problem of the identification consists of choosing an identification model and adjusting the parameters such that the response of the model approximates the response of the real system to the same input. This paper investigates the identification nonlinear system by Digital Recurrent Neural Network (DRNN). A dynamic backpropagation algorithm is employed to adapt weights and biases of the DRNN. Mathematical model based on experimental data is developed. Results of simulations show that the application of the DRN to identification of complex nonlinear structural behaviour gives satisfactory results.