Abstract
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.
Original language | English |
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Pages (from-to) | 639-650 |
Number of pages | 12 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 31 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- memristors
- neural networks (computer science)
- quaternions
- time delay systems