Stochastically asymptotic stability of delayed recurrent neural networks with both Markovian jump parameters and nonlinear disturbances

Stochastic stability analysis of piecewise homogeneous Markovian jump neural networks with mixed time-delays

“…On another active research front, time-delay systems have received a great deal of attention in recent years owing to their successful applications in a wide range of areas such as chemical processes, nuclear reactors and biological systems, and time delays may lead to instability or significantly deteriorated performances for the corresponding closed-loop systems [5][6][7][8]. Accordingly, a variety of important and interesting results have been reported on neural networks with time delays and/or parameter uncertainties based on the linear matrix inequality (LMI) approach (see, e.g., [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and the references therein).…”

control of a class of discrete-time Markov jump linear systems with piecewise-constant TPs subject to average dwell time switching

“…Numerical examples demonstrate the theoretical findings. The ideas and methods behind this paper can be thereby used to deal with the problems of the underlying system with time delays [36][37][38] or neural networks with Markovian jump parameters [39], etc. …”

Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations

“…Therefore, in Refs. [7][8][9][10][11] authors have studied stochastic perturbations on neural networks. In addition, anti-synchronization control of neural networks plays important roles in many potential applications, e.g., non-volatile memories, neuromorphic devices to simulate learning, adaptive and spontaneous behavior.…”