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Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems

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Abstract

In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonometry FLNN, Legendre FLNN (LeFLNN), Chebyshev FLNN, power series FLNN (PSFLNN) and Hermite FLNN. The recursive weights adjustment equations are derived using the combination of Lyapunov stability criterion and dynamic back propagation algorithm. In the simulation study, a total of three nonlinear systems (both static and dynamic systems) are considered for testing and comparing the approximation ability and computational complexity of the above-mentioned FLNNs. From the simulation results, it is observed that the LeFLNN has given better approximation accuracy and PSFLNN offered least computational load as compared to the rest models.

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Acknowledgements

This study is not funded by any agency.

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Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, 147004, 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

  3. Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology, New Delhi, 110020, India

    Amit Mohindru

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  1. Rajesh Kumar
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  2. Smriti Srivastava
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  3. Amit Mohindru
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Correspondence to Rajesh Kumar.

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Communicated by V. Loia.

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Kumar, R., Srivastava, S. & Mohindru, A. Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems. Soft Comput 24, 5463–5482 (2020). https://doi.org/10.1007/s00500-019-04496-0

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