Stability of delay neural networks with uncertainties via delayed intermittent control

Abstract

In this paper, we investigate the stability of neural networks with both time-varying delays and uncertainties. A novel delayed intermittent control scheme is designed to ensure the globally asymptotical stability of the addressed system. Some new delay dependent sufficient criteria for globally asymptotical stability results are derived in term of linear matrix inequalities (LMIs) by using free-weighting matrix techniques and Lyapunov-Krasovskii functional method. Finally, a numerical simulation is provided to show the effectiveness of the proposed approach.

Control of firing activities in thermosensitive neuron by activating excitatory autapse*

“…[62] Furthermore, neurons can form various functional connection loops and perform computational functions through synaptic connections. [63] As mentioned above, the autapse is widely found in cerebral cortex, visual cortex, cerebellum, striatum, and hippocampus, [39] and it has a certain effect on the ability of neurons to detect weak signals. In addition, the autaptic current plays a wide role in regulating the discharge rhythm of a single neuron and the nonlinear behavior of the network.…”

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

“…So it is important to analyze his dynamics behaviors. 8,[17][18][19][20][21][22][23] Rather than Lyapunov's classic asymptotic stability 6,[24][25][26][27] and exponential stability, 28 finite-time stability means that the system's solution trajectories converge the equilibrium point after a finite-time, and the finite-time is called the settling time or time convergence. [29][30][31][32] The finite-time stability is involved in many control problems, such as secure communication, 33 finite-time output feedback stabilization of the double integrator, 34 and the finite-time attitude tracking problem for a single spacecraft and multiple spacecraft.…”