State estimation for delayed positive neural networks under false data injection attack

This paper focuses on the state estimator for a class of delayed discrete-time positive neural networks. The utilization of network in control system for information mutual interaction brings great conveniences. But it will be surfer from the issue of information security, cyber-attacks. False data injection attacks occurring in information transmission are taken into consideration in this paper. The model of the delayed positive neural network with this kind of attacks is constructed. Through linear Lyapunov function, sufficient conditions for globally asymptotic stability of positive neural networks are proposed. The desired positive estimator is designed by putting forward to a linear programming approach. Finally, an example of application to water distribution network is given to demonstrate the effectiveness and applicability of the derived results.