Stability Analysis of Neutral-Type Hopfield Neural Networks with Multiple Delays

Abstract

AbstractThe main aim of this paper is to study the stability problem for neutral-type Hopfield neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By using a suitable Lyapunov functional, a novel sufficient stability condition is obtained for global asymptotic stability of neutral-type neural networks with multiple delays. The derived stability criterion can be expressed in terms of the parameters of the neural network model which totally relies on some simple relationships established between the network parameters and it is completely independent of time delays and neutral delays. Hence, this new global asymptotic stability condition can be easily tested and verified by using some algebraic mathematical properties. We will also make a comparison between the result of this paper and previously published corresponding results. This comparison will indicate the advantages of our proposed stability condition over the previously reported stability conditions. Since obtaining stability conditions for neutral type neural networks with multiple delays is a difficult task to achieve due to the insufficient mathematical methods and techniques, the result given in this paper can be considered an important and alternative result for this class of neutral type neural systems.KeywordsNeutral systemsDelayed neural networksStability analysisLyapunov functionals