Impulsive discrete-time GRNs with probabilistic time delays, distributed and leakage delays: an asymptotic stability issue

Stability analysis for singular random impulsive control system: A novel neutral impulsive delay system approach

“…Moreover, recent researchers have studied some special type of delay in neural networks. For example, Maharajan et al [44] discussed the leakage delay with BAM neural networks, Pandiselvi et al [45] investigated genetic regularity networks with probabilistic time delay and distributed leakage delay, Sakthivel et al [46] discussed uncertain delayed BAM neural networks, and Pratap et al [47] studied fractional order neural network with discontinuous activation function. To the best of our knowledge, there are no results based on random impulsive control for neural network, which may be our future research work.…”

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

“…In this sense, a formal stability analysis for the synchronization of complex networks, in particular for a set of oscillators, is usually given for linearized systems in a vicinity of the equilibrium point [317] by computing the stability regions in the delay parameters or using the circle criterion [318]. Some reported contributions can be found in [169,238,[319][320][321][322][323][324][325][326][327][328].…”

LMI-Based Analysis and Stabilization of Nonlinear Descriptors with Multiple Delays via Delayed Nonlinear Controller Schemes

“…ese ideas have been extended to the analysis of time-delay systems (TDSs) via LyapunovKrasovskii (L-K) functionals [7] or LyapunovRazumikhin (L-R) functions [8]. In this context, there are several results that provide sufficient stability conditions using LMI-based approaches for different classes of TDS, such as linear timedelay systems [9][10][11][12][13], uncertain linear time-delay systems [14][15][16], neutral linear systems [17][18][19][20], systems with uncertain time-invariant delays [21], descriptor system approach for TDS [22], linear parameter-varying (LPV) timedelay systems [23], systems with time-varying delays [24][25][26][27][28][29], exponential estimates for TDS [30,31], systems with polytopic-type uncertainties [32], singular systems [33], neural networks with time delay [34,35], and genetic regulatory networks with probabilistic time delays [36]. Recently, in [37] convex approaches are employed to provide robust stability conditions based on quasi-polynomials.…”