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

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

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Kartik Saini,
  • Narendra Kumar,
  • Bharat Bhushan,
  • Rajesh Kumar

摘要

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