Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays

Abstract

The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one novel sufficient stability criterion for the existence and global exponential stability of T-periodic solutions on the proposed system is obtained. In addition, an instructive numerical example is arranged to support the present approach. The obtained results broaden the application range of neutral-types inertial neural networks.

Exponential Stability of a Class of Neutral Inertial Neural Networks with Multi-Proportional Delays and Leakage Delays

“…The existence and exponential stability of the periodic solution is one important and interesting topic in the dynamical analysis of neutral neural networks. Recently, in [34], the existence and global exponential stability of T-periodic solutions of neutral-type inertial neural networks with multiple delays was investigated using the Lyapunov functional. It was found that the coefficients and the time delays are bounded constant, and the external inputs are periodic functions.…”

“…It was found that the coefficients and the time delays are bounded constant, and the external inputs are periodic functions. Consequently, the method proposed in [34] is not applicable to NINN with unbounded proportional delays and variable coefficients. Furthermore, in [35,36], almost periodic solutions for various neural networks with neutral type proportional delays and D operators were investigated by means of Banach fixed point theorem and differential inequality technique.…”

On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays

“…Si, Xie and Lie [27] investigated the global exponential stability of recurrent neural networks with piecewise constant arguments and neutral terms subject to uncertain connection weights. For more results of neutral-type neural networks, see, e.g., [28][29][30][31][32][33].…”