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A novel dynamic recurrent functional link neural network-based identification of nonlinear systems using Lyapunov stability analysis

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Abstract

In this paper, a novel dynamic recurrent functional link neural network (DRFLNN) is proposed for the identification of unknown dynamics of the nonlinear systems. The proposed structure contains a self-feedback loop(s) as well as the adjustable weighted feed-through of the input signals to the output neuron(s). A learning algorithm is developed using the combination of Lyapunov stability and dynamic back-propagation method and is applied to derive the stable parameter adjustment equations. The performance evaluation of the proposed DRFLNN model is done by comparing it with the multi-layer perceptron (consisting of a single hidden layer), radial basis function network, Elman recurrent neural network (ERNN), nonlinear auto-regressive moving average, and the conventional functional link neural network. Three benchmark systems have been used on which all these models are applied. From the results, it is found that ERNN provided better prediction accuracy as compared to the remaining models and the second-best accuracy is obtained from the proposed model. Further, the ERNN model is more complex and offers more parameters to be tuned as compared to the DRFLNN model. Thus, the training of the ERNN model is quite difficult as compared to the DRFLNN.

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Authors and Affiliations

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

    Rajesh Kumar

  2. Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology (formerly Netaji Subhas Institute of Technology), Sector 3, Dwarka, New Delhi, 110078, India

    Smriti Srivastava

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  1. Rajesh Kumar
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  2. Smriti Srivastava
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Correspondence to Rajesh Kumar.

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Kumar, R., Srivastava, S. A novel dynamic recurrent functional link neural network-based identification of nonlinear systems using Lyapunov stability analysis. Neural Comput & Applic 33, 7875–7892 (2021). https://doi.org/10.1007/s00521-020-05526-x

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