Adaptive Synchronization for a Class of Uncertain Fractional-Order Neural Networks

In this paper, synchronization for a class of uncertain fractional-order neural networks subject to external disturbances and disturbed system parameters is studied. Based on the fractional-order extension of the Lyapunov stability criterion, an adaptive synchronization controller is designed, and f...

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Bibliographic Details
Main Authors: Heng Liu, Shenggang Li, Hongxing Wang, Yuhong Huo, Junhai Luo
Format: Article
Language:English
Published: MDPI AG 2015-10-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/17/10/7185
Description
Summary:In this paper, synchronization for a class of uncertain fractional-order neural networks subject to external disturbances and disturbed system parameters is studied. Based on the fractional-order extension of the Lyapunov stability criterion, an adaptive synchronization controller is designed, and fractional-order adaptation law is proposed to update the controller parameter online. The proposed controller can guarantee that the synchronization errors between two uncertain fractional-order neural networks converge to zero asymptotically. By using some proposed lemmas, the quadratic Lyapunov functions are employed in the stability analysis. Finally, numerical simulations are presented to confirm the effectiveness of the proposed method.
ISSN:1099-4300