Stability and pinning synchronization analysis of fractional order delayed Cohen–Grossberg neural networks with discontinuous activations

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

“…where K 1 , K 2 and W are designed matrices with appropriate dimension, η is an invariable signal time delay, and sgn(t) is defined by | t |= t × sgn(t) for all t ∈ R. Remark 2: Compared with the controller designed in [30], [31], [32], [33], [34], [35], [36], [37], the designed HSMC not only has the properties of feedback SMC and sampleddata control, but also excludes f (•) and g(•) terms, keeping the property of the FNNTVD (3).…”

“…From recent results in [30], [31], [32], [33], [34], [35], [36], [37], [38], we may find that various controlling approaches are put forward, such as adaptive feedback control [30], [31], [32], active pinning control [33], [34], adaptive impulsive control [35], periodical intermittent control [36], adaptive fuzzy observer-based cooperative control [37] and sliding mode control (SMC) in [38]. Compared with the controllers in [30], [31], [32], [33], [34], [35], [36], [37], SMC can deal with uncertainties and external disturbances more effectively [39]. Thereby, many scholars adopt SMC to study the dynamic performance of linear and nonlinear system [40], [41], [42].…”

On the Stability with Respect to H-Manifolds for Cohen–Grossberg-Type Bidirectional Associative Memory Neural Networks with Variable Impulsive Perturbations and Time-Varying Delays

“…That is why numerous researchers considered delay effects on both Cohen-Grossberg and BAM neural networks, and excellent results have been reported in the literature. We will direct the reader to see [9][10][11] for some results on delayed Cohen-Grossberg neural networks, and [8,[12][13][14] for results on BAM neural networks with delays, including some very recent publications [15][16][17][18].…”

Stability of Sets Criteria for Impulsive Cohen-Grossberg Delayed Neural Networks with Reaction-Diffusion Terms

“…It is also well known that some powerful from the applied point of view neural network models, such as cellular neural networks, bidirectional neural networks, Hopfield neural networks, can be considered as special cases of CGNNs. Later on, the investigations on delayed CGNNs with constants and time-varying delays also had increased rapidly [5][6][7] including some recent results [8][9][10]. In addition, the subject of reaction-diffusion delayed CGNNs has been studied in [11][12][13][14].…”

“…Asymptotic stability of equilibrium states is one of the most important qualitative properties that has to be guaranteed in most of the applications of neural network models designed. This is why most of the existing results on neural network systems are related to asymptotic or exponential stability of equilibrium states [2][3][4][8][9][10]12,16,17,[22][23][24]30,33,35,37,45].…”