Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 47, Issue 10, Pages 2984-2994Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2676978
Keywords
Memristive recurrent neural network (MRNN); scale-limited Lagrange stability; synchronization; time scale
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Funding
- Guangdong Innovative and Entrepreneurial Research Team Program [2014ZT05G304]
- Natural Science Foundation of China [61673188]
- National Key Research and Development Program of China [2016YFB0800402]
- Science and Technology Support Program of Hubei Province [2015BHE013]
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The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison principles are established by means of inequality techniques and induction principle on time scales. Then the results concerning Lagrange stability and global finite-time synchronization of MRNNs on time scales are obtained. Scaled-limited Lagrange stability criteria are derived, in detail, via nonsmooth analysis and theory of time scales. Moreover, novel criteria for achieving the global finite-time synchronization are acquired. In addition, the derived method can also be used to study global finite-time stabilization. The proposed results extend or improve the existed ones in the literatures. Two numerical examples are chosen to show the effectiveness of the obtained results.
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