Nonlinear Structural Behaviour Identification using Digital Recurrent Neural Networks

Primljeno (Received): 2011-10-10 Prihvaćeno (Accepted): 2011-12-21 Original scientific paper Dynamical systems contain nonlinear relations which are difficult to model with conventional techniques. Hence, efficient 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 in the field of structural identification. The problem of the identification consists of choosing an identification model and adjusting the parameters in an way that the response of the model approximates the response of the real system to the same input. This paper investigates the identification of a 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 for the identification of complex nonlinear structural behaviour gives satisfactory results.