Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 6, Pages 1854-1866Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2874982
Keywords
Discrete-time neural network (NN); inertial NN; stability; synchronization; time delay; timescale
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Funding
- Guangdong Innovative and Entrepreneurial Research Team Program [2014ZT05G304]
- Natural Science Foundation of China [61673188, 61761130081]
- National Key Research and Development Program of China [2016YFB0800402]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
- Fundamental Research Funds for the Central Universities [2017KFXKJC002]
- Qatar National Research Fund [NPRP 9 166-1-031]
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This paper considers generalized discrete-time inertial neural network (GDINN). By timescale theory, the original network is rewritten as a timescale-type inertial NN. Two different scenarios are considered. In a first scenario, several criteria guaranteeing the global exponential stability for the addressed GDINN are obtained based on the generalized matrix measure concept. In this case, Lyapunov function or functional is not necessary. In a second scenario, some inequality analytical and scaling techniques are used to achieve the global exponential stability for the considered GDINN. The obtained criteria are also applied to the global exponential synchronization of drive-response GDINNs. Several illustrative examples, including applications to the pseudorandom number generator and encrypted image transmission, are given to show the effectiveness of the theoretical results.
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