• Neural Netw · Mar 2020

    A new fixed-time stability theorem and its application to the fixed-time synchronization of neural networks.

    • Chuan Chen, Lixiang Li, Haipeng Peng, Yixian Yang, Ling Mi, and Hui Zhao.
    • School of Cyber Security, Qilu University of Technology, Jinan 250353, China; Shandong Provincial Key Laboratory of Computer Networks, Jinan, 250353, China. Electronic address: chenchuan2019@qlu.edu.cn.
    • Neural Netw. 2020 Mar 1; 123: 412-419.

    AbstractIn this paper, we derive a new fixed-time stability theorem based on definite integral, variable substitution and some inequality techniques. The fixed-time stability criterion and the upper bound estimate formula for the settling time are different from those in the existing fixed-time stability theorems. Based on the new fixed-time stability theorem, the fixed-time synchronization of neural networks is investigated by designing feedback controller, and sufficient conditions are derived to guarantee the fixed-time synchronization of neural networks. To show the usability and superiority of the obtained theoretical results, we propose a secure communication scheme based on the fixed-time synchronization of neural networks. Numerical simulations illustrate that the new upper bound estimate formula for the settling time is much tighter than those in the existing fixed-time stability theorems. Moreover, the plaintext signals can be recovered according to the new fixed-time stability theorem, while the plaintext signals cannot be recovered according to the existing fixed-time stability theorems.Copyright © 2020 Elsevier Ltd. All rights reserved.

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