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Soft Computing
Erschienen in: Soft Computing 1/2019

14.05.2018 | Methodologies and Application

Comparative study of neural networks for dynamic nonlinear systems identification

verfasst von: Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, Amit Mohindru

Erschienen in: Soft Computing | Ausgabe 1/2019

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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.
Vorheriger Artikel Fuzzy multi-granulation decision-theoretic rough sets based on fuzzy preference relation
Nächster Artikel Calibrating AdaBoost for phoneme classification

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Metadaten
Titel
Comparative study of neural networks for dynamic nonlinear systems identification
verfasst von
Rajesh Kumar
Smriti Srivastava
J. R. P. Gupta
Amit Mohindru
Publikationsdatum
14.05.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 1/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3235-5

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