Nonlinear complex dynamic system identification based on a novel recurrent neural network

Abstract In this paper, a novel Modified Jordan Recurrent Neural Network (MJRNN) model is presented to identify complex nonlinear dynamical systems. The nonlinear dynamic system identification using artificial neural networks is the most commonly used method in control system engineering, due to their capabilities. The structure of the presented model is an extended version of the original Jordan recurrent neural network model. The parameter update equations are obtained by using the back-propagation optimization algorithm, which is the most frequently used method as a learning approach for the training of the proposed model's parameters. The effectiveness of the suggested neural network is evaluated in comparison to other neural networks model such as Jordan recurrent neural network(JRNN), Elman recurrent neural Network(ERNN), Diagonal recurrent neural network(DRNN), and Feedforward neural network(FFNN) model. The robustness of the proposed model is also tested with parameter variation and disturbance signal. The simulation results have shown that the proposed model performs better than the other neural network models.