Stability and Stabilization of Delayed Neural Networks with Hybrid Impulses

Abstract

In this paper, the stability and stabilization issues for a class of delayed neural networks with time-varying hybrid impulses are investigated. The hybrid effect of two types of impulses including both stabilizing and destabilizing impulses is considered simultaneously in the analysis of systems. To characterize the occurrence features of impulses, the concepts of average impulse interval and average impulse strength are employed. Based on the analysis of stability, a pinning impulsive controller which can ensure the global exponential stability of the studied neural networks is designed by pinning a small fraction of neurons. Finally, two numerical examples are given to illustrate the effectiveness of the proposed control schemes for delayed neural networks with hybrid impulses.

Exponential Synchronization of Cohen-Grossberg Neural Networks With Delays

“…Remark 1: The model (1) includes Hopfield neural network, cellular neural network, and BAM neural network as special cases. The system (1) for α j (w j (t)) = 0 are investigated in [23] and [24], the derivative of the delay is less than 1 at (1) is needed in [7]. Compared with [7], [23], and [24], the model in this paper is more general.…”

“…The system (1) for α j (w j (t)) = 0 are investigated in [23] and [24], the derivative of the delay is less than 1 at (1) is needed in [7]. Compared with [7], [23], and [24], the model in this paper is more general.…”

“…Therefore, it is necessary to consider the influence of these two factors on the system's stability. In recent years, some results have been obtained on the asymptotic behavioral properties of impulsive neural networks [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. The exponential convergence of impulsive inertial neural networks is explored in [17], the exponential stability of impulsive complex-valued neural networks is discussed in [18] and [19], the robust passivity and stability of uncertain complex-valued impulsive neural network is investigated in [20], and the exponential synchronization of neural networks is proposed in [21].…”

State Estimation for Standard Neural Network Models with Time‐Varying Delays

“…e delay-dependent stability analysis of recurrent neural networks has been researched widely [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and the references therein.…”