New passivity criteria for memristive uncertain neural networks with leakage and time-varying delays

Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays

“…Therefore, since 2008, its potential applications have become more and more popular in many aspects such as generation computer, powerful brain-like neural computer, and so on. There is no doubt that it has initiated the worldwide concern with the emergence of the memristor (see [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]). For the neural networks, the first job is considering whether they are stable or not.…”

“…Moreover, as far as we know, the passivity theory plays an important role in the analysis of the stability of dynamical systems, nonlinear control, and other areas. Thus, some researchers have investigated passivity or dissipativity criteria on MRNNs (see [14,16,[23][24][25][27][28][29][30]).…”

Secondary delay‐partition approach on robust performance analysis for uncertain time‐varying Lurie nonlinear control system

“…Meanwhile, TDSs have received considerable interest for their extensive applications in practical systems, such as mechanics, economy, neural networks, physics, medicine, biology, and engineering systems. Thus, it is theoretically and practically important to study the dynamical systems with time delay …”

“…Thus, it is theoretically and practically important to study the dynamical systems with time delay. To date, many researchers have invested a lot of time and effort to investigate the problem of stability analysis for TDSs in the real engineering systems, such as singular systems, 4,5 Markov jump systems, 6,7 synchronization issues, 8,9 sliding mode control, 10 robust nonfragile filtering, 11 H ∞ tracking, 12 uncertain neural networks, 13 and other scientific areas. 14,15 The existing results of TDSs can be classified into 2 categories: delay-independent and delay-dependent ones.…”

Stability Analysis for Stochastic Neutral‐Type Memristive Neural Networks with Time‐Varying Delay and S‐Type Distributed Delays

“…In practical application, it is more common for a dynamic system with time-varying delay, because the constant delay is only an ideal approximation of the timevarying delay. Many scholars have made great achievements in this respect (see [8][9][10][11]). Here, we have to point out that neural networks are composed of a large number of neurons, many of which are clustered into spherical or layered structures and interact with each other and are connected to a variety of complex neural pathways through the axon.…”