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Comparative study of neural networks for dynamic nonlinear systems identification

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Abstract

In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Bharati Vidyapeeth’s College of Engineering, A-4, Paschim Vihar, New Delhi, 110063, India

    Rajesh Kumar

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

    Smriti Srivastava & J. R. P. Gupta

  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. J. R. P. Gupta
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  4. Amit Mohindru
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Correspondence to Rajesh Kumar.

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This study is not funded by any agency.

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Rajesh Kumar declares that he has no conflict of interest. Smriti Srivastava declares that she has no conflict of interest. J.R.P Gupta declares that he has no conflict of interest. Amit Mohindru declares that he has no conflict of interest.

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Kumar, R., Srivastava, S., Gupta, J.R.P. et al. Comparative study of neural networks for dynamic nonlinear systems identification. Soft Comput 23, 101–114 (2019). https://doi.org/10.1007/s00500-018-3235-5

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