Lagrange exponential stability for fuzzy Cohen–Grossberg neural networks with time-varying delays

Multistability of discrete-time delayed Cohen–Grossberg neural networks with second-order synaptic connectivity

“…Due to their potential applications in associative memory, image processing, pattern recognition, and some other areas, the studies of this kind of neural networks and their applications have attracted a tremendous amount of research interests [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13].…”

Synchronous Analysis for Fuzzy Coupled Neural Networks with Column Pinning Controllers

“…Fuzzy theory [10][11][12] is considered an efficient tool to solve vagueness problems of the complex systems. Compared with the traditional NNS, the FCNNS have advantages for their capabilities in handling uncertain information and representing nonlinear dynamics [13][14][15].…”

Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays

“…Different stability concepts as an important topic were defined in the existing references, such as exponential stability [2,7,12,13], robust stability [14,15], complete stability [16], multistability [17,18], Lagrange stability [19,20], and asymptotic stability [21].…”

“…Although we consider that the time delays arise frequently in practical systems, it is difficult to measure them precisely. Up until now, there have been some results about the stability of complex-valued neural networks with time-varying delay (see [6,7,13,19,22,23] and the references therein). It is well known that a neural network usually has a spatial nature due to the presence of an amount of parallel pathways of a variety of axon sizes and lengths; it is desired to model them by introducing continuously distributed delays over a certain duration of time such that the distant past has less influence compared with the recent behavior of the state [2].…”