Boundedness and Stability for Discrete‐Time Delayed Neural Network with Complex‐Valued Linear Threshold Neurons

Abstract

The discrete-time delayed neural network with complex-valued linear threshold neurons is considered. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique and analysis method, several new delay-dependent criteria for checking the boundedness and global exponential stability are established. Illustrated examples are also given to show the effectiveness and less conservatism of the proposed criteria.

Global μ-stability of complex-valued delayed neural networks with leakage delay

“…[28], the boundedness and stability for discrete-time delayed neural network with complexvalued linear threshold neurons have been studied, where the activation function is supposed to be in the form as follows:…”

Global asymptotic stability for a class of complex-valued Cohen–Grossberg neural networks with time delays

“…Based on the new condition and linear matrix inequality, some new criteria to ensure the existence, uniqueness and global asymptotic stability of the equilibrium point of complex-valued RNNs with time delays are established. In [8] and [9], the discrete-time delayed neural networks with complex-valued linear threshold neurons have been studied and several criteria on the boundedness, global attractivity and global exponential stability were obtained.…”

Stability criterion of complex-valued neural networks with both leakage delay and time-varying delays on time scales

“…In recent years, there have been some researches on the stability of various CVNNs, for example, see [10]- [21]. In [10], authors proposed a CVNNs, and its weight matrix was supposed to be Hermitian with nonnegative diagonal entries in order to preserve the stability of the network.…”