期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 3, 页码 911-922出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2877410
关键词
Synchronization; Artificial neural networks; Adaptation models; Brain modeling; Delay effects; Delays; Adaptive systems; Adaptive control; image encryption; linear matrix inequality (LMI); neural networks (NNs); reaction-diffusion; synchronization
类别
资金
- National Research Foundation of Korea (NRF) - Ministry of Science and the ICT KRF Project [2017H1D3A1A01014107]
- NRF - Ministry of Education [NRF-2016R1A6A1A03013567]
- National Research Foundation of Korea [2017H1D3A1A01014107] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper is mainly concerned with the synchronization problem of reaction-diffusion neural networks (RDNNs) with delays and its direct application in image secure communications. An adaptive control is designed without a sign function in which the controller gain matrix is a function of time. The synchronization criteria are established for an error model derived from master-slave models through solving the set of linear matrix inequalities derived by constructing the suitable novel Lyapunov-Krasovskii functional candidate, Green's formula, and Wirtinger's inequality. If the proposed sufficient conditions are satisfied, then the global asymptotic synchronization of the error model is guaranteed. The numerical illustrations are provided to demonstrate the validity of the derived synchronization criteria. In addition, the role of system parameters is picturized through the chaotic nature of RDNNs and those unprecedented solutions is utilized to promote better security of image transactions. As is evident, the enhancement of image encryption algorithm is designed with two levels, namely, image watermarking and diffusion process. The contributions of this paper are discussed as concluding remarks.
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