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Soft Computing

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07.11.2019 | Methodologies and Application

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

verfasst von: Rajesh Kumar, Smriti Srivastava, Amit Mohindru

Erschienen in: Soft Computing | Ausgabe 7/2020

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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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Titel: Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems
verfasst von: Rajesh Kumar
Smriti Srivastava
Amit Mohindru
Publikationsdatum: 07.11.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04496-0

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