Finite-time projective synchronization of memristor-based delay fractional-order neural networks

Finite-time projective synchronization of memristor-based delay fractional-order neural networks This paper mainly investigates the finite-time projective synchronization problem of memristor-based delay fractional-order neural networks (MDFNNs). By using the definition of finite-time projective synchronization, combined with the memristor model, set-valued map and differential inclusion theory, Gronwall–Bellman integral inequality and Volterra-integral equation, the finite-time projective of MDFNNs is achieved via the linear feedback controller. Novel sufficient conditions are obtained to guarantee the finite-time projective synchronization of the drive-response MDFNNs. Besides, we also analyze the feasible region of the settling time. Finally, two numerical examples are given to show the effectiveness of the proposed results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

Finite-time projective synchronization of memristor-based delay fractional-order neural networks

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3613-z
Publisher site
See Article on Publisher Site

Abstract

This paper mainly investigates the finite-time projective synchronization problem of memristor-based delay fractional-order neural networks (MDFNNs). By using the definition of finite-time projective synchronization, combined with the memristor model, set-valued map and differential inclusion theory, Gronwall–Bellman integral inequality and Volterra-integral equation, the finite-time projective of MDFNNs is achieved via the linear feedback controller. Novel sufficient conditions are obtained to guarantee the finite-time projective synchronization of the drive-response MDFNNs. Besides, we also analyze the feasible region of the settling time. Finally, two numerical examples are given to show the effectiveness of the proposed results.

Journal

Nonlinear DynamicsSpringer Journals

Published: Jun 20, 2017

References

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