This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses

Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects

In this paper, the globally exponential synchronization of delayed complex dynamical networks with impulsive and stochastic perturbations is studied The concept named “average impulsive interval” with “elasticity number” of impulsive sequence is introduced to get a less conservative synchronization criterion By comparing with existing results, in which maximum or minimum of impulsive intervals are used to derive the synchronization criterion, the proposed synchronization criterion increases (or decreases) the impulse distances, which leads to the reduction of the control cost (or enhance the robustness of anti-interference) as the most important characteristic of impulsive synchronization techniques It is discovered in our criterion that “elasticity number” has influence on synchronization of delayed complex dynamical networks but has no influence on that of non-delayed complex dynamical networks Numerical simulations including a small-world network coupled with delayed Chua’s circuit are given to show the effectiveness and less conservativeness of the theoretical results

Synchronization of delayed complex dynamical networks with impulsive and stochastic effects

p th moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control

This paper is a contribution to the analysis of the pth moment exponential synchronization problem for a class of stochastic delayed Cohen–Grossberg neural networks with Markovian switching. The jumping parameters are determined by a continuous-time, discrete-state Markov chain, and the delays are time-varying delays. By using the Lyapunov–Krasovskii functional, stochastic analysis theory, a generalized Halanay-type inequality as well as output coupling with delay feedback control technique, some novel sufficient conditions are derived to achieve complete pth moment exponential synchronization of the addressed neural networks. In particular, the traditional assumptions on the differentiability of the time varying delay and the boundedness of its derivative are removed in this paper. The results obtained in this paper generalize and improve many known results. Moreover, a numerical example and its simulation are also provided to demonstrate the effectiveness and applicability of the theoretical results.

pth moment exponential synchronization for stochastic delayed Cohen–Grossberg neural networks with Markovian switching

This note develops Razumikhin-type theorems on pth moment exponential stability for general impulsive stochastic delay differential equations. Some applications are also given to some special types of impulsive stochastic delay differential equations. Two examples illustrate the effectiveness of the proposed results.

Razumikhin-Type Theorems on $p$ th Moment Exponential Stability of Impulsive Stochastic Delay Differential Equations

Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays

Measurement of Layer-Specific Mechanical Properties of Intact Blood Vessels Based on Intravascular Optical Coherence Tomography

We optimize digital pre-emphasis for narrow optical filtering using the balance among electrical RMS, equalization-enhanced noise and quantization, and experimentally show a low-complexity cutoff approach to enhance SNR in strong pre-emphasis.

Qingyi Guo

Papers

Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays

Measurement of Layer-Specific Mechanical Properties of Intact Blood Vessels Based on Intravascular Optical Coherence Tomography

Low-Complexity RMS-enhanced Digital Pre-emphasis under Limited Transmitter Power and ENoB

Molecular simulation of the adsorption and diffusion characteristics of CH4 in coal subjected to N-Methylpyrrolidone treatment