Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 2, Pages 639-650Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2908755
Keywords
Delays; Neurons; Biological neural networks; Memristors; Quaternions; Linear matrix inequalities; Stability criteria; Exponential passivity; nondecomposition approach; quaternion-valued memristor-based neural networks (QVMNNS); time-varying delay
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Funding
- National Natural Science Foundation of China [61603125, 11872175]
- Australian Research Council [DP120104986]
- Chinese Scholarship Council [201708410029]
- Key Program of Henan Universities [17A120001]
- Xinhe Huang Tingfang Young Scholars' Fund of HUEL [hncjzfdxxhhtf201913]
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This paper is concerned with the problem of global exponential passivity for quaternion-valued memristor-based neural networks (QVMNNs) with time-varying delay. The QVMNNs can be seen as a switched system due to the memristor parameters are switching according to the states of the network. This is the first time that the global exponential passivity of QVMNNs with time-varying delay is investigated. By means of a nondecomposition method and structuring novel Lyapunov functional in form of quaternion self-conjugate matrices, the delay-dependent passivity criteria are derived in the forms of quaternion-valued linear matrix inequalities (LMIs) as well as complex-valued LMIs. Furthermore, the asymptotical stability criteria can be obtained from the proposed passivity criteria. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results.
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