Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111624
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Type: Journal article
Title: Synchronization of general chaotic neural networks with nonuniform sampling and packet missing: a switched system approach
Author: Lu, R.
Shi, P.
Su, H.
Wu, Z.
Lu, J.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(3):523-533
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Renquan Lu, Peng Shi, Hongye Su, Zheng-Guang Wu, and Jianquan Lu
Abstract: This paper is concerned with the exponential synchronization issue of general chaotic neural networks subject to nonuniform sampling and control packet missing in the frame of the zero-input strategy. Based on this strategy, we make use of the switched system model to describe the synchronization error system. First, when the missing of control packet does not occur, an exponential stability criterion with less conservatism is established for the resultant synchronization error systems via a superior time-dependent Lyapunov functional and the convex optimization approach. The characteristics induced by nonuniform sampling can be used to the full because of the structure and property of the constructed Lyapunov functional, that is not necessary to be positive definite except sampling times. Then, a criterion is obtained to guarantee that the general chaotic neural networks are synchronous exponentially when the missing of control packet occurs by means of the average dwell-time technique. An explicit expression of the sampled-data static output feedback controller is also gained. Finally, the effectiveness of the proposed new design methods is shown via two examples.
Keywords: Chaotic systems; neural networks; packet missing, sampled-data control; switched systems
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030083945
DOI: 10.1109/TNNLS.2016.2636163
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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