Delay-dependent Criterion For Global Stability Research Articles

Delay-dependent global robust stability criterion for neural networks with neutral-type time-varying delays

The problem of global robust stability for neural networks with neutral-type time-varying delays is investigated in this paper. Neutral differential equations are used to construct this class of neural networks. The time-varying delay function is bounded, and it is not required to be continuously differentiable. A new delay-dependent global stability criterion is derived based on the Lyapunov-Krasovskii functional approach. The criterion is formulated in terms of a linear matrix inequality. Numerical examples are given to illustrate the validity of the proposed stability criterion.

On improved delay-dependent criterion for global stability of bidirectional associative memory neural networks with time-varying delays

In the paper, the global robust asymptotic stability of bidirectional associative memory (BAM) neural networks with time-varying delays and uncertainties is investigated. A novel stability criterion is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. A numerical example is illustrated to show the effectiveness of our new result.