Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach

Lag quasi-synchronization for memristive neural networks with switching jumps mismatch

“…Recently, many efforts have been devoted to the dynamics of MNNs, because they not only have rich applications in signal, image processing and diversified applications elsewhere, but also can facilitate to simulate the real brain [9][10][11][12][13][14][15][16][17][18]. The synchronization problem of MNNs has attracted considerable attention due to its strong applications [19][20][21][22][23][24][25][26][27][28]. It is worth noting that MNNs are a class of statedependent switched systems.…”

“…In this paper, we revisit the synchronization problem of the following MNNs, which has been discussed in [23][24][25]:…”

“…Thus, in order to describe the memory function and capture the pinched hysteretic feature of memristor, following [23][24][25], the definitions of a ij ðx j ðtÞÞ and b ij ðx j ðt À sÞÞ are assumed to be…”

“…Compared with the continuous artificial neural networks, MNNs have many evident advantages. In secure communications, the MNNs chaotic is safer [12,20,23,[25][26][27]. In memory storage, the MNNs shall possess more memory storage capacity [10,11].…”

Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

“…Especially, the memristor-based neural networks has been one of the most active research areas and has attracted the attention of many researchers (Itoh and Chua 2010;Pershin and Ventra 2012;Yang et al 2014;Qi et al 2014;Chandrasekar et al 2014;Wan and Cao 2015). Memristor-based neural network can remember its past dynamical history, store a continuous set of states, and be ''plastic'' according to the pre-synaptic and postsynaptic neuronal activity (Strukov et al 2008;Qi et al 2014), an ideal tool to mimic the functionalities of the human brain.…”