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Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems

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Abstract

A novel fully connected recurrent neural network (FCRNN) structure is proposed for the identification of unknown dynamics of nonlinear systems. The proposed recurrent structure consists of internal feedback layers of adjustable weights which impart necessary memory property to the structure and improves its ability in handling the dynamical systems. The back-propagation algorithm (BP) is used to derive the weight update equations of the proposed model. The convergence of the proposed approach is proven in the sense of Lyapunov-stability analysis. A total of three examples are considered and the performance of the proposed structure is evaluated by comparing it with the results obtained from other popular neural network models such as feed-forward neural network (FFNN), Elman neural network (ENN), Jordan neural network (JNN), and the locally recurrent neural networks (LRNN). Experimental results obtained show that the FCRNN model has outperformed the other neural models in terms of identification accuracy and robustness.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India

    R. Shobana & Bhavnesh Jaint

  2. Department of Electrical Engineering, National Institute of Technology Kurukshetra, Kurukshetra, 136119, India

    Rajesh Kumar

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  1. R. Shobana
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  2. Bhavnesh Jaint
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  3. Rajesh Kumar
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Contributions

The authors, Shobana. R, Bhavnesh Jaint and Rajesh Kumar, have contributed to the study, conception, design, material preparation, and analysis. The whole draft of the manuscript was written jointly by Shobana. R, Bhavnesh Jaint and Rajesh Kumar.

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Correspondence to Rajesh Kumar.

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Shobana, R., Jaint, B. & Kumar, R. Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems. Soft Comput 28, 2737–2751 (2024). https://doi.org/10.1007/s00500-023-09187-5

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