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Neural Computing and Applications
Published in: Neural Computing and Applications 4/2019

19-07-2017 | Original Article

Fractional neural observer design for a class of nonlinear fractional chaotic systems

Authors: Amin Sharafian, Reza Ghasemi

Published in: Neural Computing and Applications | Issue 4/2019

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Abstract

In this paper, a novel observer structure for nonlinear fractional-order systems is presented to estimate the states of fractional-order nonlinear chaotic system with unknown dynamical model. A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability. The main contribution is the extension of neural observer for fractional dynamics in a way that satisfies Miattag–Leffler conditions. Observer design procedure guarantees the convergence of observer error to the neighborhood of zero. Furthermore, the robustness of the proposed estimator against uncertainties and external disturbances are the main benefits of the proposed method. Simulation results show the effectiveness and capabilities of the proposed observer.
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Metadata
Title
Fractional neural observer design for a class of nonlinear fractional chaotic systems
Authors
Amin Sharafian
Reza Ghasemi
Publication date
19-07-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 4/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3153-y

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