Global Stability Analysis for a Class of Cohen-Grossberg Neural Network Models

Abstract

Abstract. By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability of the Cohen-Grossberg neural network models. The condition contains and improves some of the previous results in the earlier references.

Exponential Stability of Antiperiodic Solution for BAM Neural Networks with Time-Varying Delays

“…In real life, many BAM neural networks are affected by external perturbation. There are many papers concerned with stochastic BAM neural networks, e.g., [36][37][38][39][40][41]. In the future, we will give our research on these networks.…”

Stability and stabilization for stochastic Cohen‐Grossberg neural networks with impulse control and noise‐induced control

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] For example, Cohen and Grossberg 1 studied the absolute stability of competitive neural networks; Cao and Wang 3 and Guo 4 investigated global asymptotic stability of recurrent neural networks. The authors in other works [5][6][7][8][9][10][11] dealt with global stability of several kinds of Cohen-Grossberg neural networks. The Cohen-Grossberg models were firstly given and considered by Cohen and Grossberg, 1 which have been widely applied in lots of engineering and scientific fields such as neural biology, parallel computing, associative memory, signal, and image processing.…”

Exponential Lagrange Stability for Markovian Jump Uncertain Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Impulsive Control

“…As a special class of mathematical models, neural networks are similar to the brain synapses link structure; neural networks possess multiple dynamic behaviors [14]. For these reasons, neural frameworks have received considerable attention as a result of their intensive applications in determination of some optimization issue, associative memory, classification of patterns, and other areas [15][16][17][18]. Since axonal signal transmission time delays often occur in various neural networks and may also cause undesirable dynamic network behaviors such as oscillation and instability, thus, it is important to study the stability of neural networks.…”