LMI Approach to Robust Stability Analysis of Cohen-Grossberg Neural Networks with Multiple Delays

Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen–Grossberg neural networks with mixed delays

“…Therefore, it is essential to investigate the stability and robustness of the network against such intrinsic parameter deviations and external perturbations [5,12,15,[20][21][22]27,29,30,37,44,47,53,54]. Zhao et al [37] have studied robust stability for discrete-time uncertain Markovian Jumping neural networks with defective statistics of modes without diffusion.…”

“…During the implementation of artificial neural networks on verylarge-scale integrated chips, transmitting time delays are unavoidable and may cause undesirable dynamic network behaviors, such as oscillation and instability. Hence, it is important to consider the stability of CGNNs with delays [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In real nerve systems, a neural network could be stabilized or destabilized by certain stochastic inputs [18].…”