On global robust stability for a class of static recurrent neural networks with delays;

This paper analyzes the robust stability of variable delayed Cohen-grossberg neural network by the Lyapunov stability theory and linear matrix inequality(LMI) and obtains the sufficient conditions of discriminating global robust stability.

In this paper,the global robust stability of the uncertain recurrent neural networks with delay is investigated.

The condition for robust exponential stability was formulated as a generalized eigenvalued problem,which established an estimation of the exponential convergence rate and improved the previous results.

The global robust exponential stability is investigated for interval neural networks with mixed delay.

The global asymptotic robust stability of interval cellular neural networks with S-type distributed delays is investigated.

Some useful criteria for the global robust stability of the delayed interval static neural networks are obtained by using Lyapunov functional method and topological degree theory.

Periodic Solutions、Almost Periodic Solutions and Global Exponential Stability for Cellular Neural Networks with Delays;

Based on the relationship between matrix and symmetric matrix global exponential stability of the discrete-time neural networks model and the result of exponential convergence rate were obtained by using the characteristics of eigenvalues of a positive definite matrix and introducing a proper factor.

Based on the extended Hanalay s inequality and the upper-right derivative,the global exponential stability for the second order Hopfield neural networks with time delays was investigated.