Global exponential stability of memristive Cohen–Grossberg neural networks with mixed delays and impulse time window

Analysis of Exponential Stability for Neutral Stochastic Cohen-Grossberg Neural Networks with Mixed Delays

“…Over the last few years, Cohen-Grossberg neural networks have been paid more and more attention because of their potential applications in various fields such as signal processing, image processing, pattern recognition, associative memory, programming problems, and combinatorial optimization (see [1][2][3][4][5]). Cohen-Grossberg neural networks model was first introduced by Cohen and Grossberg in 1983, which has become one of the most important neural network models (see [6,7]). Compared with recurrent neural networks, Hopfield neural networks, and cellular neural networks, it is more challenging and interesting to build Cohen-Grossberg neural networks model.…”

“…Based onAssumptions 2,3,4,5,6, and 13, the trivial solution of system(24) is mean-square exponentially input-to-state stable, if there exist positive constants , , V , ( = 1, 2, . .…”

Pinning Synchronization via Intermittent Control for Memristive Cohen-Grossberg Neural Networks With Mixed Delays

“…Especially, the studies on memristive CGNNs (MCGNNs) in the stability [27], [29], [30] and synchronization [24], [25], [31], [32] attract more attention. Among them, it is worth studying on synchronization for a class of MCGNNs in the field of secure communications, image and data encryption.…”

Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays

“…Many scholars have devoted themselves to the study of dynamical behavior of MNNs, such as synchronization [5], stability and stabilization [6], [7], passivity [8]- [12], dispassvity [13], [14], etc. Up to now, many scholars have proposed many kinds of memristor-based neural networks models, such as reaction-diffusion memristor-based neural networks [5], [8], [12], [15], inertial memristor-based neural networks [9], [10], Hopfield memristor-based neural networks [7], VOLUME 4, 2016 Cohen-Grossberg memristor-based neural networks [16]- [18], fractional-order memristor-based neural networks [19]- [23], etc. Synchronization and stabilization are considered to be the most important dynamical behavior of MNNs.…”